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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 OSTI2014

Highly-­laminated, high-­saturation-flux-­density, magnetic cores for on-­chip inductors in power converter applications

The objective of this project is development of a manufacturing process to realize thick highly-laminated metallic magnetic cores with suppressed eddy current losses for power applications and demonstration of integrated magnetic components using the process. The continual reduction in size and increase in functionality of portable electronic devices drives the development of ultracompact electrical power converters and therefore their associated inductors, which typically occupy the largest volume within the converter. This inductor size reduction can be achieved by increasing both the converter switching frequency as well as inductor operating flux density by utilizing appropriate magnetic core materials. Magnetically soft metallic alloys (e.g., NiFe and CoNiFe) are good core material candidates due to their superior magnetic properties (e.g., high saturation flux density and low coercivity) compared to conventional ferrite. However, to minimize eddy current losses, the core thickness is limited to the skin depth (typically less than 3 µm for 10 MHz operation); cores of this thickness are likely insufficient for handling high power (> 10W). Therefore, there is great interest in the development of highly-laminated metallic alloy cores with single lamination thickness in the sub-micron range to achieve suppressed eddy-current losses and high power handling capacity simultaneously. During the project, highly-laminated metallic alloy cores comprising up to 2000 layers of permalloy (Ni 80 Fe 20 ) or CoNiFe materials with single lamination thickness less than 1 µm have been fabricated based on automated multilayer electrodeposition technology compatible with CMOS manufacturing. The developed highly-laminated metallic cores preserve the high saturation flux density and low coercivity characteristics of metallic alloys, while suppressing eddy-current losses less than 1 W/cm3 at operation flux density of 0.5 T and frequency over 1 MHz. The developed highly-laminated metallic alloy cores have been further integrated into microfabricated windings to realize compact, on-chip inductors. Utilizing CMOS-compatible integration techniques, circular (toroid) and bar-shape (solenoid) microinductors have been fabricated exhibiting a constant inductance higher than 1 µH up to 50 MHz with peak quality factor as high as 35 at 5 MHz. The microfabricated inductor with highly-laminated core was tested in a power converter and demonstrated a converter efficiency of 93% at 5.5 MHz and 42 W under 100V input and 35V output operation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2013

Analysis of Benefits of an Energy Imbalance Market in the NWPP

The Northwest Power Pool (NWPP) Market Assessment Committee (MC) Initiative, which was officially launched on March 19, 2012, set out to explore a range of alternatives that could help the Balancing Authorities and scheduling utilities in the NWPP area address growing operational and commercial challenges affecting the regional power system. The MC formed an Analytical Team with technical representatives from each of the member Balancing Areas in the NWPP and with staff of Pacific Northwest National Laboratory (PNNL). This Analytical Team was instructed to conduct extensive studies of intra-hour operation of the NWPP system in the year 2020 and of the NWPP region with 14,671 MW of wind penetration. The effort utilized a sub-hourly production cost model (the PLEXOS® computer model) that inputs data from the Western Electricity Coordinating Council (WECC)-wide Production Cost Model (PCM) to evaluate potential production cost savings. The Analytical Team was given two general options to evaluate: •Energy Imbalance Market (EIM): establishment of an automated, organized NWPP area market for economically supplying energy imbalance within the hour. •Enhanced Market-Operational Tools (EMT) that might augment or replace an EIM. The Analytical The Analytical Team built on the WECC-wide PCM data from prior work done in the WECC and carried forward the evolution of the original WECC Transmission Expansion Planning Policy Committee (TEPPC) 2020 PC0 data base. A large number of modifications and improvements were made to this case and the data were subjected to extensive review by the team members to improve the model representation of the Northwest (NW). MC meetings that were open to the public were held for interested parties to review and provide input to the study. Results for the test, base, and sensitivity case studies performed by the MC Initiative Analytical Team indicate that there are a wide range of benefits that could be obtained from the operation of an EIM in the NWPP depending on what assumptions are made. The instructions from the MC were to determine a "minimum high confidence" range of potential benefits. The results for the Base Case indicate that the EIM benefits ranged from approximately $40 million to $70 million in annual savings from the operation of an EIM in the NWPP footprint. A number of additional relevant sensitivity cases were performed, including low and high water conditions, low and high natural gas prices, and various flex reserve requirements, resource operations, and amounts of resource capability held back during the preschedule period. Along with the results for the Base Case, the results for these studies yielded EIM benefits that clustered within the range of $70 to $80 million dollars per year with potential benefits ranging from approximately $125 million to as little as $17 million per year. Because the design and operation of an EIM could enable participating Balancing Authorities (BAs) to collectively lower the quantity of resources they must carry to meet within-hour balancing needs, a sensitivity case was also performed to analyze the impact that such reductions might have on the benefits from an EIM. The results for this sensitivity case indicate that such reductions could increase the benefits from the operation of an EIM in the NWPP into the range of approximately $130 million to $160 million per year. Also, a sensitivity case for a WECC-wide EIM was performed with the results indicating that the potential benefits to the NWPP could increase into the range of $197 million to $233 million per year. While there may be potential reliability benefits from the coordinated dispatch process underlying the operation of an EIM, reliability benefits from an EIM were out of the scope of this study. The EIM benefit analyses that were performed by the Analytical Team are provided in this report.

Power markets, NWPP, Energy Imbalance, EIM, Renewa↗
DOE OSTI2016

An Advanced ISOCS In Situ Gamma Spectrometry Services Tool to Reduce Uncertainties in Waste Characterization Projects - 16311

Canberra's In Situ Object Counting System (ISOCS) is an established tool used for gamma spectrometry allowing a physical representation of complex geometries and mathematical calculation of the calibration function. It avoids the need for radioisotope standards which can be costly and prohibitively difficult to produce. Uncertainties in the sample - detector geometry lead to uncertainties in the calibrations. Canberra's ISOCS Uncertainty Estimator (IUE) tool offers an automated means of accommodating known model geometry uncertainties, with calculation of robust defensible calibration uncertainties. However these uncertainties may be large leading to potential over-estimation of under-estimation of activity. This can be equated directly to waste disposal costs if one is using ISOCS for waste sentencing to classify wastes as (for example) Transuranic Waste (TRU) or Low Level Waste (LLW). An Advanced ISOCS in-situ Gamma Spectrometry (AIGS) services tool has been developed to reduce the uncertainties. This system is based on generating and comparing geometry models that yield Figures of Merit (FoM) indicative of improved consistency between modelled data and available diverse measurement data. This paper describes the development and features of AIGS, focusing on recent testing with radioisotope sources in a 200 litre drum filled with a simulated waste matrix. The results demonstrate a significant advantage for Am-241 where uncertainties can otherwise amount to a factor of 2 or more. We show how our specialist teams can develop procedures for deployment of standard ISOCS hardware, adapting measurement geometries and procedures through careful planning, to provide data that can be used to greatly reduce the uncertainties in the measured activities. Lastly, we comment on the range of potential applications for this new technique and the wider benefits for the decommissioning industry. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

CRDIAC: Coupled Reactor Depletion Instrument with Automated Control

When modeling the behavior of a nuclear reactor over time, it is important to understand how the isotopes in the reactor will change, or transmute, over that time. This is especially important in the reactor fuel itself. Many nuclear physics modeling codes model how particles interact in the system, but do not model this over time.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Design of an advanced energy management system for microgrid control using a state machine

A state machine is proposed as the solution for an automated microgrid energy management system (EMS) to improve transient performance during transition operations. It characterizes microgrid operation by seven states that cover all the operating modes: two for steady-state operation (grid-connected and islanded), four for transition operation (preparing for disconnection, transitioning to islanding, preparing for reconnection, and transitioning to grid-connected), and one for emergency operation (black-start operation). A unique dispatch algorithm is developed for each state to achieve the control objective, and the transition function is implemented in the state machine as control logics to transition the system from one state to the next. The feasibility and effectiveness of the developed state machine is validated by simulation in MATLAB with an example microgrid, and the test results show excellent performance of the state machine to achieve the target control objective in each state and to improve the system's transient performance during transition operation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Synthetic proteins for COVID-19 diagnostics

There is an urgent need for inexpensive, rapid and specific antigen-based assays to test for vaccine efficacy and detect infection with SARS-CoV-2 and its variants. We have identified a small, synthetic protein (JS7), representing a region of maximum variability within the receptor binding domain (RBD), which binds antibodies in sera from nine patients with PCR-verified COVID-19 of varying severity. Antibodies binding to either JS7 or the SARS-CoV-2 recombinant RBD, as well as those that disrupt binding between a fragment of the ACE2 receptor and the RBD, are proportional to disease severity and clinical outcome. Binding to JS7 was inhibited by linear peptides from the RBD interface with ACE2. Variants of JS7, such as E484K or N501Y, can be quickly synthesized in pure form in large quantities by automated methods. JS7 and related synthetic antigens can provide a basis for specific diagnostics for SARS-CoV-2 infections.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

hi_class background evolution, initial conditions and approximation schemes

Cosmological datasets have great potential to elucidate the nature of dark energy and test gravity on the largest scales available to observation. Theoretical predictions can be computed with hi_class (www.hiclass-code.net), an accurate, fast and flexible code for linear cosmology, incorporating a wide range of dark energy theories and modifications to general relativity. We introduce three new functionalities into hi_class: (1) Support for models based on covariant Lagrangians, including a constraint-preserving integration scheme for the background evolution and a series of worked-out examples: Galileon, nKGB, quintessence (monomial, tracker) and Brans-Dicke. (2) Consistent initial conditions for the scalar-field perturbations in the deep radiation era, identifying the conditions under which modified-gravity isocurvature perturbations may grow faster than adiabatic modes leading to a loss of predictivity. (3) An automated quasistatic approximation scheme allowing order-of-magnitude improvement in computing performance without sacrificing accuracy for wide classes of models. These enhancements bring the treatment of dark energy and modified gravity models to the level of detail comparable to software tools restricted to standard $\Lambda$CDM cosmologies. Finally, the hi_class code is publicly available (https://github.com/miguelzuma/hi_class_public), ready to explore current data and prepare for next-generation experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Enabling the Electrification of Offshore Activities – Co-Demonstration of Next-Generation Autonomous Offshore Power System and Resident, Uncrewed Mobile and Static Assets at PacWave Wave Energy Test Site

Oceans cover two-thirds of the earth's surface and form the world's biggest and best – yet largely untapped – battery. Ocean waves have more energy density than other renewables, including wind, solar, and biomass, and have the potential to supply 4x the world's annual energy consumption (Masterson, 2022; Zic, 2020). In addition to the impact wave energy can have on decarbonizing and diversifying the electric grid, it offers a significant value proposition in the emerging blue economy sector (LiVecchi et al, 2019). The blue economy consists of industries operating offshore, including shipping, oil and gas, defense and security, aquaculture, and research. These industries require bringing people and energy on site to perform daily work, but current energy costs in the blue economy are extremely high. Here, the prevailing processes are complex, including shore dependencies and fuel transportation logistics. Few alternatives for reliable power generation exist, with the most prominent being high cost and high carbon emissions diesel generation. Because of this lack of affordable, reliable power, the trends of electrification, digitization, and automation that have led to substantial innovation and improvements in the terrestrial economy over the last two decades are slow to come to the blue economy.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Rapid monitoring of fermentations: a feasibility study on biological 2,3-butanediol production

2,3-butanediol (2,3-BDO) is an economically important platform chemical that can be produced by the fermentation of sugars using an engineered strain of Zymomonas mobilis . These fermentations require continuous monitoring and modification of fermentation conditions to maximize 2,3-BDO yields and minimize the production of the undesired coproducts glycerol and acetoin. Because of the time required for sampling and off-line chromatographic measurement of fermentation samples, the ability of fermentation scientists to modify fermentation conditions in a timely manner is limited. The goal of this study was to test if near-infrared spectroscopy (NIRS) along with multivariate statistics could reduce the time needed for this analysis and enable real-time monitoring and control of the fermentation. In this work we developed partial least squares (PLS) calibration models to predict the concentrations of glucose, xylose, 2,3-BDO, acetoin, and glycerol in fermentations via NIRS using two different spectrometers and two different spectroscopy modalities. We first evaluated the feasibility of rapid NIRS monitoring through experiments where we measured the signals from each analyte of interest and built NIRS-based PLS models using spectra from synthetic samples containing uncorrelated concentrations of these analytes. All analytes showed unique spectral signatures, and this initial modeling showed that all analytes could be detected simultaneously. We then began work with samples from laboratory fermentation experiments and tested the feasibility of regression model development across two spectral collection modalities (at-line and on-line) and two instruments: a laboratory-grade instrument and a low-cost instrument with a more limited spectral range. All modalities showed promise in the ability to monitor Z. mobilis fermentations of glucose and xylose to 2,3-BDO. The low-cost instrument displayed a lower signal-to-noise ratio than the laboratory-grade instrument, which led to comparatively lower performance overall, but still provided sufficient accuracy to monitor fermentation trends. While the ease of use of on-line monitoring systems was favored as compared to at-line systems due to the lack of sampling required and potential for automated process control, we observed some decrease in performance due to the additional complexity of the sample matrix. We have demonstrated that NIRS combined with multivariate analysis can be used for at-line and on-line monitoring of the concentrations of glucose, xylose, 2,3-BDO, acetoin, and glycerol during Z. mobilis fermentations. The decrease in signal-to-noise ratio when using a low-cost spectrometer led to greater prediction error than the laboratory-grade spectrometer for at-line monitoring. The on-line monitoring modality showed great promise for real time process control via NIRS.

09 BIOMASS FUELS↗
DOE OSTI2020

Machine Learning for Automated Extraction of Building Geometry

As data science comes to buildings, the promise of using machine learning and novel sources of data has received much attention. Advances in machine learning and computer vision algorithms, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics – cost-effectively, and at scale. Acquisition of features such as footprint are time consuming and costly to acquire with today’s manual methods, but can be streamlined through intelligent software-based solutions applied to satellite images. When combined with aerial RGB and thermal images, full 3D geometries and thermal maps can be constructed to determine additional characteristics such as window to wall ratio, height, number of stories and envelope thermal characteristics. In this paper we present three contributions to accelerate these high potential opportunities: (1) a methodical analysis of how these features can be integrated into today’s simulation and data driven software tools to enhance efficiency measure identification and owner/operator decision making; (2) development and accuracy testing of open source deep neural network methods to extract building footprints from satellite imagery, including the curation and application of openly available GIS datasets for training and continued development by others; and (3) an open framework for drone-based image capture and creation of 3D building geometries. This work represents an important bridge between high-level studies that span diverse application areas and those that detail point solutions yet cannot be easily replicated or extended.

Touzani, Samir↗
DOE OSTI2022

Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow

We report, in fluid physics, data-driven models to enhance or accelerate time to solution are becoming increasingly popular for many application domains, such as alternatives to turbulence closures, system surrogates, or for new physics discovery. In the context of reduced order models of high-dimensional time-dependent fluid systems, machine learning methods grant the benefit of automated learning from data, but the burden of a model lies on its reduced-order representation of both the fluid state and physical dynamics. In this work, we build a physics-constrained, data-driven reduced order model for Navier–Stokes equations to approximate spatiotemporal fluid dynamics in the canonical case of isotropic turbulence in a triply periodic box. The model design choices mimic numerical and physical constraints by, for example, implicitly enforcing the incompressibility constraint and utilizing continuous neural ordinary differential equations for tracking the evolution of the governing differential equation. We demonstrate this technique on a three-dimensional, moderate Reynolds number turbulent fluid flow. In assessing the statistical quality and characteristics of the machine-learned model through rigorous diagnostic tests, we find that our model is capable of reconstructing the dynamics of the flow over large integral timescales, favoring accuracy at the larger length scales. More significantly, comprehensive diagnostics suggest that physically interpretable model parameters, corresponding to the representations of the fluid state and dynamics, have attributable and quantifiable impact on the quality of the model predictions and computational complexity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Improving deep learning-based protein distance prediction in CASP14

Abstract Motivation Accurate prediction of residue–residue distances is important for protein structure prediction. We developed several protein distance predictors based on a deep learning distance prediction method and blindly tested them in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The prediction method uses deep residual neural networks with the channel-wise attention mechanism to classify the distance between every two residues into multiple distance intervals. The input features for the deep learning method include co-evolutionary features as well as other sequence-based features derived from multiple sequence alignments (MSAs). Three alignment methods are used with multiple protein sequence/profile databases to generate MSAs for input feature generation. Based on different configurations and training strategies of the deep learning method, five MULTICOM distance predictors were created to participate in the CASP14 experiment. Results Benchmarked on 37 hard CASP14 domains, the best performing MULTICOM predictor is ranked 5th out of 30 automated CASP14 distance prediction servers in terms of precision of top L/5 long-range contact predictions [i.e. classifying distances between two residues into two categories: in contact (<8 Angstrom) and not in contact otherwise] and performs better than the best CASP13 distance prediction method. The best performing MULTICOM predictor is also ranked 6th among automated server predictors in classifying inter-residue distances into 10 distance intervals defined by CASP14 according to the precision of distance classification. The results show that the quality and depth of MSAs depend on alignment methods and sequence databases and have a significant impact on the accuracy of distance prediction. Using larger training datasets and multiple complementary features improves prediction accuracy. However, the number of effective sequences in MSAs is only a weak indicator of the quality of MSAs and the accuracy of predicted distance maps. In contrast, there is a strong correlation between the accuracy of contact/distance predictions and the average probability of the predicted contacts, which can therefore be more effectively used to estimate the confidence of distance predictions and select predicted distance maps. Availability and implementation The software package, source code and data of DeepDist2 are freely available at https://github.com/multicom-toolbox/deepdist and https://zenodo.org/record/4712084#.YIIM13VKhQM. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

SU-F-BRD-10: Lung IMRT Planning Using Standardized Beam Bouquet Templates

Purpose: We investigate the feasibility of choosing from a small set of standardized templates of beam bouquets (i.e., entire beam configuration settings) for lung IMRT planning to improve planning efficiency and quality consistency, and also to facilitate automated planning. Methods: A set of beam bouquet templates is determined by learning from the beam angle settings in 60 clinical lung IMRT plans. A k-medoids cluster analysis method is used to classify the beam angle configuration into clusters. The value of the average silhouette width is used to determine the ideal number of clusters. The beam arrangements in each medoid of the resulting clusters are taken as the standardized beam bouquet for the cluster, with the corresponding case taken as the reference case. The resulting set of beam bouquet templates was used to re-plan 20 cases randomly selected from the database and the dosimetric quality of the plans was evaluated against the corresponding clinical plans by a paired t-test. The template for each test case was manually selected by a planner based on the match between the test and reference cases. Results: The dosimetric parameters (mean±S.D. in percentage of prescription dose) of the plans using 6 beam bouquet templates and those of the clinical plans, respectively, and the p-values (in parenthesis) are: lung Dmean: 18.8±7.0, 19.2±7.0 (0.28), esophagus Dmean: 32.0±16.3, 34.4±17.9 (0.01), heart Dmean: 19.2±16.5, 19.4±16.6 (0.74), spinal cord D2%: 47.7±18.8, 52.0±20.3 (0.01), PTV dose homogeneity (D2%-D99%): 17.1±15.4, 20.7±12.2 (0.03).The esophagus Dmean, cord D02 and PTV dose homogeneity are statistically better in the plans using the standardized templates, but the improvements (<5%) may not be clinically significant. The other dosimetric parameters are not statistically different. Conclusion: It's feasible to use a small number of standardized beam bouquet templates (e.g. 6) to generate plans with quality comparable to that of clinical plans. Partially supported by NIH/NCI under grant #R21CA161389 and a master research grant by Varian Medical System.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Continuous Fiber Composite Electrofusion Couplers (Final Report)

The objective of this three-year project was to develop a pipe coupler for high-pressure (100bar service, 350bar burst) composite hydrogen pipelines, without the use of elastomeric sealing mechanisms (e.g. o-rings), thereby avoiding seal maintenance costs and effort associated with digging up subterranean lines over a pipe’s lifetime of up to 50 years. The project aimed to build on past designs at (TRL 3), to adapt their use to hydrogen delivery and test in a relevant environment (TRL 5). This work is toward the DOE’s objective to reduce the technical barrier of high as-installed cost of pipelines, by utilizing fiber reinforced plastic (FRP) pipe. Further DOE targets include those from the Fuel Cell Technologies Office Multi-Year Research, Development, and Demonstration Plan: Milestone 1.5: Coordinating with the H2 Production and Storage sub-programs, identify optimized delivery pathways that meet a H2 delivery and dispensing cost of <\$ 2/gge for use in consumer vehicles. (4Q, 2020) Milestone 6.3: By 2020, reduce the cost of hydrogen delivery from the point of production to the point of use in consumer vehicles to <\$ 2/gge of hydrogen for the gaseous delivery pathway (4Q, 2020). This project headed by Automated Dynamics (now Trelleborg) with partners Savanah River National Laboratory, and NOV Fiber Glass Systems, emerged due to a similar previous project with the DOE, completed by SNRL and NOV Fiber Glass Systems (formerly Fiberspar) in 2014, that evaluated the mechanical suitability of FRP (composite) lines for these same high pressures. That projected concluded with a favorable outlook on using composite lines and required a suitable pipe coupler. The resulting coupler design from this current project employs electrofusion due to its proven use on low pressure unreinforced lines and for its simplicity in the field. This electrofusion coupler design utilizes established concepts for pipe couplers and combines them in a new and unique method. Existing pipe couplers are employed on thermoset pipes in high pressure, with elastomeric seals as the sealing mechanism, and on thermoplastic low-pressure pipes by way of electrofusion. However, no EF coupler is available for composite pipelines for 350bar hydrogen containment.

08 HYDROGEN↗
DOE OSTI2025

Integration of LIBS with Machine Learning for Real-Time Monitoring of Feedstock in H 2 Gasification Applications

This project, funded by the U.S. Department of Energy (DOE) – Office of Fossil Energy under Award Number DE-FE0032177, aimed to assess the feasibility of an integrated Laser-Induced Breakdown Spectroscopy (LIBS) system with advanced machine learning (ML) models for real-time characterization and potential control of hydrogen gasifiers running on waste materials as feedstocks. This was a multidisciplinary effort that encompassed the acquisition and standardized analysis of individual and blended feedstocks—comprising biomass, coal waste, and plastic waste, followed by the development of a dynamic LIBS bench system for material sample analysis and development of predictive ML models. Comprehensive laboratory testing enabled the creation of a robust elemental dataset that served as the foundation for ML model training. Techniques such as Random Forest, Gradient Boosting, Support Vector Regression, and Neural Networks were employed to predict key feedstock properties, including higher heating value (HHV), moisture content, thermal conductivity, and ash composition with high accuracy. The results were validated against experimental data and demonstrated strong potential for real-time application in gasifier control systems. The project concluded with a study on the integration of the LIBS+ML approach for gasifier control and a techno-economic analysis of the implementation of the approach into hydrogen (H 2 ) gasification systems. Dissemination of results was carried out at a DOE meeting. This work establishes a scalable framework for automated, in-line feedstock quality assessment, offering significant implications for process optimization and emissions reduction in hydrogen production.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2016

Accuracy of automated measurement and verification (M&V) techniques for energy savings in commercial buildings

Trustworthy savings calculations are critical to convincing investors in energy efficiency projects of the benefit and cost-effectiveness of such investments and their ability to replace or defer supply-side capital investments. However, today’s methods for measurement and verification (M&V) of energy savings constitute a significant portion of the total costs of efficiency projects. They also require time-consuming manual data acquisition and often do not deliver results until years after the program period has ended. The rising availability of “smart” meters, combined with new analytical approaches to quantifying savings, has opened the door to conducting M&V more quickly and at lower cost, with comparable or improved accuracy. These meter- and software-based approaches, increasingly referred to as “M&V 2.0”, are the subject of surging industry interest, particularly in the context of utility energy efficiency programs. Program administrators, evaluators, and regulators are asking how M&V 2.0 compares with more traditional methods, how proprietary software can be transparently performance tested, how these techniques can be integrated into the next generation of whole-building focused efficiency programs. This paper expands recent analyses of public-domain whole-building M&V methods, focusing on more novel M&V2.0 modeling approaches that are used in commercial technologies, as well as approaches that are documented in the literature, and/or developed by the academic building research community. We present a testing procedure and metrics to assess the performance of whole-building M&V methods. We then illustrate the test procedure by evaluating the accuracy of ten baseline energy use models, against measured data from a large dataset of 537 buildings. The results of this study show that the already available advanced interval data baseline models hold great promise for scaling the adoption of building measured savings calculations using Advanced Metering Infrastructure (AMI) data. Median coefficient of variation of the root mean squared error (CV(RMSE)) was less than 25% for every model tested when twelve months of training data were used. With even six months of training data, median CV(RMSE) for daily energy total was under 25% for all models tested. Finally, these findings can be used to build confidence in model robustness, and the readiness of these approaches for industry uptake and adoption

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Final Project Report: Composition and Realization of Source-to-Sink High-Performance Flows: File Systems, Storage, Hosts, LAN and WAN

A number of Department of Energy (DOE) science applications, involving exascale computing systems and large experimental facilities, are expected to generate large volumes of data, in the range of petabytes to exabytes, which will be transported over wide-area networks for the purpose of storage, visualization, and analysis. The objectives of this proposal are to (1) develop and test the component technologies and their synthesis methods to achieve source-to-sink high-performance flows, and (2) develop tools that provide these capabilities through simple interfaces to users and applications. In terms of the former, we propose to develop (1) optimization methods that align and transition multiple storage flows to multiple network flows on multicore, multibus hosts; and (2) edge and long-haul network path realization and maintenance using advanced provisioning methods including OSCARS and OpenFlow. We also propose synthesis methods that combine these individual technologies to compose high-performance flows using a collection of constituent storage-network flows, and realize them across the storage and local network connections as well as long-haul connections. We propose to develop automated user tools that profile the hosts, storage systems, and network connections; compose the source-to-sink complex flows; and set up and maintain the needed network connections.

97 MATHEMATICS AND COMPUTING↗