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DOE OSTI2014

Data and Analytics to Inform Energy Retrofit of High Performance Buildings

Buildings consume more than one-third of the world?s primary energy. Reducing energy use in buildings with energy efficient technologies is feasible and also driven by energy policies such as energy benchmarking, disclosure, rating, and labeling in both the developed and developing countries. Current energy retrofits focus on the existing building stocks, especially older buildings, but the growing number of new high performance buildings built around the world raises a question that how these buildings perform and whether there are retrofit opportunities to further reduce their energy use. This is a new and unique problem for the building industry. Traditional energy audit or analysis methods are inadequate to look deep into the energy use of the high performance buildings. This study aims to tackle this problem with a new holistic approach powered by building performance data and analytics. First, three types of measured data are introduced, including the time series energy use, building systems operating conditions, and indoor and outdoor environmental parameters. An energy data model based on the ISO Standard 12655 is used to represent the energy use in buildings in a three-level hierarchy. Secondly, a suite of analytics were proposed to analyze energy use and to identify retrofit measures for high performance buildings. The data-driven analytics are based on monitored data at short time intervals, and cover three levels of analysis ? energy profiling, benchmarking and diagnostics. Thirdly, the analytics were applied to a high performance building in California to analyze its energy use and identify retrofit opportunities, including: (1) analyzing patterns of major energy end-use categories at various time scales, (2) benchmarking the whole building total energy use as well as major end-uses against its peers, (3) benchmarking the power usage effectiveness for the data center, which is the largest electricity consumer in this building, and (4) diagnosing HVAC equipment using detailed time-series operating data. Finally, a few energy efficiency measures were identified for retrofit, and their energy savings were estimated to be 20percent of the whole-building electricity consumption. Based on the analyses, the building manager took a few steps to improve the operation of fans, chillers, and data centers, which will lead to actual energy savings. This study demonstrated that there are energy retrofit opportunities for high performance buildings and detailed measured building performance data and analytics can help identify and estimate energy savings and to inform the decision making during the retrofit process. Challenges of data collection and analytics were also discussed to shape best practice of retrofitting high performance buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

New analytical solutions to the two-phase water faucet problem

Here, the one-dimensional water faucet problem is one of the classical benchmark problems originally proposed by Ransom to study the two-fluid two-phase flow model. With certain simplifications, such as massless gas phase and no wall and interfacial frictions, analytical solutions had been previously obtained for the transient liquid velocity and void fraction distribution. The water faucet problem and its analytical solutions have been widely used for the purposes of code assessment, benchmark and numerical verifications. In our previous study, the Ransom’s solutions were used for the mesh convergence study of a high-resolution spatial discretization scheme. It was found that, at the steady state, an anticipated second-order spatial accuracy could not be achieved, when compared to the existing Ransom’s analytical solutions. A further investigation showed that the existing analytical solutions do not actually satisfy the commonly used two-fluid single-pressure two-phase flow equations. In this work, we present a new set of analytical solutions of the water faucet problem at the steady state, considering the gas phase density’s effect on pressure distribution. This new set of analytical solutions are used for mesh convergence studies, from which anticipated second-order of accuracy is achieved for the 2nd order spatial discretization scheme. In addition, extended Ransom’s transient solutions for the gas phase velocity and pressure are derived, with the assumption of decoupled liquid and gas pressures. Numerical verifications on the extended Ransom’s solutions are also presented.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

ExactPack: A python library of exact analytic solutions

Verification of multi-physics simulation software against problems with known analytic or semi-analytic solutions is an important aspect of research into a wide variety of fields involving the motion of fluids, shock physics and other dynamic material properties. Previous work comparing simulation results against analytic solutions has been ad-hoc, with developers frequently writing their own analytic solvers. This has resulted in a large amount of duplicated effort. The python library ExactPack has been developed as a collection of analytic and semi-analytic solvers to a variety of multi-physics problems, providing a consistent API to a set of well-tested solver implementations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Comparison of analytic models of instability of rarefied gas flow in a channel

Numerical and analytical results are compared concerning the limit properties of the trajectories, attractors and bifurcations of rarefied gas flows in channels. The cascade of bifurcations obtained in our previous analytical and numerical investigations is simulated numerically for different scattering functions V generalizing the ray-diffuse reflection of gas particles from the surface. The main purpose of numerical simulation by Monte Carlo method is the investigation of the properties of different analytic nonlinear dynamic systems corresponding to rarefied gas flow in a channel. The results are compared as well for the models suggested originally by R. N. Miroshin, as for the approximations considered for the first time or for studied in our subsequent papers. Analytical solutions we obtained earlier for the ray reflection which means only one determined velocity of scattered from the walls gas atoms, generally different from the specular reflection. The nonlinear iterative equation describing a rarefied gas flow in a long channel becomes unstable in some regions of corresponding parameters of V (it means the sensitivity to boundary conditions). The values of the parameters are found from analytical approximations in these regions. Numerical results show that the chaotic behavior of the nonlinear dynamic system corresponds to strange attractors and distinguishes clearly from Maxwellian distribution and from the equilibrium on the whole. In the regions of instability (as the dimension of the attractor increases) the search for a corresponding state requires a lot more computation time and a lot of data (the amount of data required increases exponentially with embedding dimension). Therefore the main complication in the computation is reducing as well the computing time as the amount of data to find a suitably close solution. To reduce the computing time our analytical results are applied. Flow conditions satisfying the requirements to the experiment are indicated where the instability of considered type can be detected. The advantages and the drawbacks of considered approximations are detected and the features of obtained solutions are indicated. The recommendations are given for applying the results in practical applications and in numerical calculations of rarefied gas flows.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

Integrated Multi-Scale Data Analytics and Machine Learning for the Distribution Grid

We consider the field of machine learning and where it is both useful, and not useful, for the distribution grid and buildings interface. While analytics, in general, is a growing field of interest, and often seen as the golden goose in the burgeoning distribution grid industry, its application is often limited by communications infrastructure, or lack of a focused technical application. Overall, the linkage of analytics to purposeful application in the grid space has been limited. In this paper we consider the field of machine learning as a subset of analytical techniques, and discuss its ability and limitations to enable the future distribution grid. To that end, we also consider the potential for mixing distributed and centralized analytics and the pros and cons of these approaches. There is an exponentially expanding volume of measured data being generated on the distribution grid, which, with appropriate application of analytics, may be transformed into intelligible, actionable information that can be provided to the right actors - such as grid and building operators, at the appropriate time to enhance grid or building resilience, efficiency, and operations against various metrics or goals - such as total carbon reduction or other economic benefit to customers. While some basic analysis into these data streams can provide a wealth of information, computational and human boundaries on performing the analysis are becoming significant, with more data and multi-objective concerns. Efficient applications of analysis and the machine learning field are being considered in the loop. This paper describes benefits and limits of present machine-learning applications for use on the grid and presents a series of case studies that illustrate the potential benefits of developing advanced local multi-variate analytics machine-learning-based applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Mortar: An open testbed for portable building analytics

Access to large amounts of real-world data has long been a barrier to the development and evaluation of analytics applications for the built environment. Open data sets exist, but they are limited in their span (how much data is available) and context (what kind of data is available and how it is described). Evaluation of such analytics is also limited by how the analytics themselves are implemented, often using hard-coded names of building components, points and locations, or unique input data formats. To advance the methodology for how such analytics are implemented and evaluated, we present Mortar: an open testbed for portable building analytics, currently spanning 90 buildings and containing over 9.1 billion data points. All buildings in the testbed are described using Brick, a recently developed metadata schema, providing rich functional descriptions of building assets and subsystems. We also propose a simple architecture for writing portable analytics applications that are robust to the diversity of buildings and can configure themselves based on context. We demonstrate the utility of Mortar by implementing 11 applications from the literature.

data set↗

DOE OSTI2016

Analytic Solution for Time-Dependent Neutron Transport with Delayed Neutrons via Singular Eigenfunction Expansion

An important way to verify the proper functioning of a computer code, and its underlying numerical schemes, is by comparing numerical results generated with the code to an analytic solution. To this end, we present an analytic solution for time-dependent neutron transport with delayed neutrons, which can be used to verify computer codes for modeling reactor kinetics phenomena. To develop our analytic solution, we use the singular eigenfunction expansion method. Ganapol et al. have also developed a solution to this problem, but by using the multiple collision technique. Our approach has certain advantages with respect to this previous work, in particular that it yields a closed form (i.e., in terms of an integral rather than an infinite series) explicit expression for the angular flux (i.e., instead of just the scalar flux or the angular flux at a single spatial point). In addition, Su and Olson have presented an analytic solution for a very similar problem: non-equilibrium radiative transfer. Their approach involves a Laplace transform, which they invert by showing there are no singularities in the right complex plane of the Laplace transform parameter and then simply choosing the Bromwich contour as the imaginary axis. To extend this previous work to our case, we would further need to exclude supercritical problems in order to use the same contour. The resulting analytic solution would then be of limited interest. In contrast, our analytic solution is completely general with respect to sub, delayed super, or prompt supercriticality. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

A note on the analytic structure of celestial amplitudes

Celestial amplitudes, obtained by applying Mellin transform and analytic continuation on “ordinary” amplitudes, have interesting properties which may provide useful insights on the underlying theory. Their analytic structures are thus of great interest and need to be better understood. In this paper, we critically examine the analytic structure of celestial amplitudes in a massless low-energy effective field theory. We find that, fixed-order loop contributions, which generate multipoles on the negative β-plane, in general do not provide an accurate description of the analytic structure of celestial amplitudes. By resumming over the leading logarithmic contributions using renormalization group equations (RGEs), we observe much richer analytic structures, which generally contain branch cuts. It is also possible to generate multipoles or shifted single poles if the RGEs satisfy certain relations. Including sub-leading logarithmic contributions is expected to introduce additional corrections to the picture. However, without a new approach, it is difficult to make a general statement since the analytic form of the Mellin transform is challenging to obtain.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

Semi-analytical sensitivity analysis for nonlinear transient problems

Efficient analytical sensitivity computations are essential elements of gradient-based optimization schemes; however, they can be difficult to implement. This implementation issue is often resolved by adopting the semi-analytical method which exhibits the efficiency of the analytical methods and the ease of implementation of the finite difference method. However, care must be taken as semi-analytical sensitivities may exhibit errors due to truncation and round-off. Additional errors are introduced if the convergence tolerance of the primal analysis is not sufficiently small. This paper gives a general overview and some new developments of the analytical and semi-analytical sensitivity analyses for nonlinear steady-state, transient, and dynamic problems. We discuss the restrictive assumptions, accuracy, and consistency of these methods. Both adjoint and direct differentiation methods are studied. Numerical examples are provided.

42 ENGINEERING↗

DOE OSTI2018

Quantitative Comparison of Ternary Eutectic Phase-Field Simulations with Analytical 3D Jackson–Hunt Approaches

For the analytical description of the relationship between undercoolings, lamellar spacings and growth velocities during the directional solidification of ternary eutectics in 2D and 3D, different extensions based on the theory of Jackson and Hunt are reported in the literature. Besides analytical approaches, the phase-field method has been established to study the spatially complex microstructure evolution during the solidification of eutectic alloys. The understanding of the fundamental mechanisms controlling the morphology development in multiphase, multicomponent systems is of high interest. For this purpose, a comparison is made between the analytical extensions and three-dimensional phase-field simulations of directional solidification in an ideal ternary eutectic system. Based on the observed accordance in two-dimensional validation cases, the experimentally reported, inherently three-dimensional chain-like pattern is investigated in extensive simulation studies. The results are quantitatively compared with the analytical results reported in the literature, and with a newly derived approach which uses equal undercoolings. A good accordance of the undercooling–spacing characteristics between simulations and the analytical Jackson–Hunt apporaches are found. The results show that the applied phase-field model, which is based on the Grand potential approach, is able to describe the analytically predicted relationship between the undercooling and the lamellar arrangements during the directional solidification of a ternary eutectic system in 3D.

36 MATERIALS SCIENCE↗

DOE OSTI2013

FlexIO: I/O Middleware for Location-Flexible Scientific Data Analytics

Increasingly severe I/O bottlenecks on High-End Computing machines are prompting scientists to process simulation output data online while simulations are running and before storing data on disk. There are several options to place data analytics along the I/O path: on compute nodes, on separate nodes dedicated to analytics, or after data is stored on persistent storage. Since different placements have different impact on performance and cost, there is a consequent need for flexibility in the location of data analytics. The FlexIO middleware described in this paper makes it easy for scientists to obtain such flexibility, by offering simple abstractions and diverse data movement methods to couple simulation with analytics. Various placement policies can be built on top of FlexIO to exploit the trade-offs in performing analytics at different levels of the I/O hierarchy. Experimental results demonstrate that FlexIO can support a variety of simulation and analytics workloads at large scale through flexible placement options, efficient data movement, and dynamic deployment of data manipulation functionalities.

Computer Science↗

DOE OSTI2017

Rethinking Visual Analytics for Streaming Data Applications

In the age of data science, the use of interactive information visualization techniques has become increasingly ubiquitous. From online scientific journals to the New York Times graphics desk, the utility of interactive visualization for both storytelling and analysis has become ever more apparent. As these techniques have become more readily accessible, the appeal of combining interactive visualization with computational analysis continues to grow. Arising out of a need for scalable, human-driven analysis, primary objective of visual analytics systems is to capitalize on the complementary strengths of human and machine analysis, using interactive visualization as a medium for communication between the two. These systems leverage developments from the fields of information visualization, computer graphics, machine learning, and human-computer interaction to support insight generation in areas where purely computational analyses fall short. Over the past decade, visual analytics systems have generated remarkable advances in many historically challenging analytical contexts. These include areas such as modeling political systems [Crouser et al. 2012], detecting financial fraud [Chang et al. 2008], and cybersecurity [Harrison et al. 2012]. In each of these contexts, domain expertise and human intuition is a necessary component of the analysis. This intuition is essential to building trust in the analytical products, as well as supporting the translation of evidence into actionable insight. In addition, each of these examples also highlights the need for scalable analysis. In each case, it is infeasible for a human analyst to manually assess the raw information unaided, and the communication overhead to divide the task between a large number of analysts makes simple parallelism intractable. Regardless of the domain, visual analytics tools strive to optimize the allocation of human analytical resources, and to streamline the sensemaking process on data that is massive, complex, incomplete, and uncertain in scenarios requiring human judgment.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

An Analytical Solution for the Initiation and Early Progression of Fretting Wear in Spherical Contacts

Abstract This article derives analytical solutions to calculate the wear volume at the initiation of fretting motion and its early progression over the first few oscillation cycles. The Archard-based model considers a deformable hemisphere that is contact with a deformable flat block. The material pairs investigated are special alloys, the Inconel 617/Incoloy 800H, and Inconel 617/Inconel 617. The analytical study begins with a unidirectional frictional sliding contact, where the local interfacial sliding distance and the nominal sliding distance at the initiation of gross slip are derived. The obtained analytical expressions for unidirectional sliding are then used to derive the corresponding wear volume for the initiation and early progression of gross slip and the wear volume for a general fretting cycle under elastic conditions. These analytical derivations are all verified by the finite element analysis (FEA). The FEA method and the analytical solutions render virtually identical results for both similar and dissimilar material pairs. The effects of plasticity on the wear volume under elastic–plastic conditions are also investigated. It is found that the fretting wear volumes obtained from the FEA simulations, which include plasticity, are close to those obtained from the analytical expressions for purely elastic regimes. All the results are presented in normalized forms, which can be easily generalized and applied to three-dimensional fretting wear of other material pairs.

Engineering↗

DOE OSTI2013

Monte Carlo and analytical model predictions of leakage neutron exposures from passively scattered proton therapy

Purpose: Stray neutron radiation is of concern after radiation therapy, especially in children, because of the high risk it might carry for secondary cancers. Several previous studies predicted the stray neutron exposure from proton therapy, mostly using Monte Carlo simulations. Promising attempts to develop analytical models have also been reported, but these were limited to only a few proton beam energies. The purpose of this study was to develop an analytical model to predict leakage neutron equivalent dose from passively scattered proton beams in the 100-250-MeV interval.Methods: To develop and validate the analytical model, the authors used values of equivalent dose per therapeutic absorbed dose (H/D) predicted with Monte Carlo simulations. The authors also characterized the behavior of the mean neutron radiation-weighting factor, w{sub R}, as a function of depth in a water phantom and distance from the beam central axis.Results: The simulated and analytical predictions agreed well. On average, the percentage difference between the analytical model and the Monte Carlo simulations was 10% for the energies and positions studied. The authors found that w{sub R} was highest at the shallowest depth and decreased with depth until around 10 cm, where it started to increase slowly with depth. This was consistent among all energies.Conclusion: Simple analytical methods are promising alternatives to complex and slow Monte Carlo simulations to predict H/D values. The authors' results also provide improved understanding of the behavior of w{sub R} which strongly depends on depth, but is nearly independent of lateral distance from the beam central axis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2014

SU-E-T-479: Development and Validation of Analytical Models Predicting Secondary Neutron Radiation in Proton Therapy Applications

Purpose: Test and validation of analytical models predicting leakage neutron exposure in passively scattered proton therapy. Methods: Taking inspiration from the literature, this work attempts to build an analytical model predicting neutron ambient dose equivalents, H*(10), within the local 75 MeV ocular proton therapy facility. MC simulations were first used to model H*(10) in the beam axis plane while considering a closed final collimator and pristine Bragg peak delivery. Next, MC-based analytical model was tested against simulation results and experimental measurements. The model was also expended in the vertical direction to enable a full 3D mapping of H*(10) inside the treatment room. Finally, the work focused on upgrading the literature model to clinically relevant configurations considering modulated beams, open collimators, patient-induced neutron fluctuations, etc. Results: The MC-based analytical model efficiently reproduced simulated H*(10) values with a maximum difference below 10%. In addition, it succeeded in predicting measured H*(10) values with differences <40%. The highest differences were registered at the closest and farthest positions from isocenter where the analytical model failed to faithfully reproduce the high neutron fluence and energy variations. The differences remains however acceptable taking into account the high measurement/simulation uncertainties and the end use of this model, i.e. radiation protection. Moreover, the model was successfully (differences < 20% on simulations and < 45% on measurements) extended to predict neutrons in the vertical direction with respect to the beam line as patients are in the upright seated position during ocular treatments. Accounting for the impact of beam modulation, collimation and the present of a patient in the beam path is far more challenging and conversion coefficients are currently being defined to predict stray neutrons in clinically representative treatment configurations. Conclusion: Analytical models predicting secondary neutrons in proton therapy represent a promising solution that substitute for time-consuming MC calculations.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Mortar: An Open Testbed for Portable Building Analytics

Access to large amounts of real-world data has long been a barrier to the development and evaluation of analytics applications for the built environment. Open datasets exist, but they are limited in their span (how much data is available) and context (what kind of data is available and how it is described). Evaluation of such analytics is also limited by how the analytics themselves are implemented, often using hard-coded names of building components, points and locations, or unique input data formats. To advance the methodology for how such analytics are implemented and evaluated, we present Mortar: an open testbed for portable building analytics, currently spanning 90 buildings and containing over 9.1 billion data points. All buildings in the testbed are described using Brick, a recently developed metadata schema, providing rich functional descriptions of building assets and subsystems. We also propose a simple architecture for writing portable analytics applications that are robust to the diversity of buildings and can configure themselves based on context. We demonstrate the utility of Mortar by implementing 11 applications from the literature.

47 OTHER INSTRUMENTATION↗

DOE OSTI2015

Improving the trust in results of numerical simulations and scientific data analytics

This white paper investigates several key aspects of the trust that a user can give to the results of numerical simulations and scientific data analytics. In this document, the notion of trust is related to the integrity of numerical simulations and data analytics applications. This white paper complements the DOE ASCR report on Cybersecurity for Scientific Computing Integrity by (1) exploring the sources of trust loss; (2) reviewing the definitions of trust in several areas; (3) providing numerous cases of result alteration, some of them leading to catastrophic failures; (4) examining the current notion of trust in numerical simulation and scientific data analytics; (5) providing a gap analysis; and (6) suggesting two important research directions and their respective research topics. To simplify the presentation without loss of generality, we consider that trust in results can be lost (or the results’ integrity impaired) because of any form of corruption happening during the execution of the numerical simulation or the data analytics application. In general, the sources of such corruption are threefold: errors, bugs, and attacks. Current applications are already using techniques to deal with different types of corruption. However, not all potential corruptions are covered by these techniques. We firmly believe that the current level of trust that a user has in the results is at least partially founded on ignorance of this issue or the hope that no undetected corruptions will occur during the execution. This white paper explores the notion of trust and suggests recommendations for developing a more scientifically grounded notion of trust in numerical simulation and scientific data analytics. We first formulate the problem and show that it goes beyond previous questions regarding the quality of results such as V&V, uncertainly quantification, and data assimilation. We then explore the complexity of this difficult problem, and we sketch complementary general approaches to address it. This paper does not focus on the trust that the execution will actually complete. The product of simulation or of data analytic executions is the final element of a potentially long chain of transformations, where each stage has the potential to introduce harmful corruptions. These corruptions may produce results that deviate from the user-expected accuracy without notifying the user of this deviation. There are many potential sources of corruption before and during the execution; consequently, in this white paper we do not focus on the protection of the end result after the execution.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

High Performance Data Analytics on a Commodity Private Cloud

Sandia is researching novel methods of analysis for large datasets. The term “Big Data” has been used frequently in recent years to describe a discipline of computer science that attempts to distil an increasing quantity of data into a result or meaningful subset that can be easily visualized and acted upon by human consumers. These data are frequently characterized by one or more of the following: high volume, velocity, variety, and veracity. Sandia scientists and researchers are no strangers to large datasets, which often typify the result of high-performance computing workloads. The challenge for Sandia data scientists, however, comes at the arrival of exascale computing, which has become mandated by executive order for national computing centers. Because of power and networking limitations, exascale computing precludes the movement of datasets at exascale from data production to data analysis. Such analysis must be done in-situ, to mitigate the time and power costs of network transport. Currently, many of Sandia’s data analytics environments exist as independent entities, often specialized for individual use cases, interconnected to other data analytics, high-performance computing, and storage systems. Sandia is procuring, installing, and researching future platforms for such data analytics. A memory footprint of 256 GB per node, fourteen data rate (FDR) InfiniBand networking, PCIe connected non-volatile memory (NVMe) data storage, and an object data store will characterize the coming institutional data analytics platforms. The combined systems will contain 300 compute nodes. A cloud-inspired software stack resulting from collaboration between the Sandia centers will allow the systems to support a flexible mix of user and system-facing workloads and use cases including code development and testing in R and Python, Apache Hadoop and Spark, distributed in-memory databases, graph analytics, emulytics, system log and predictive analytics, and data visualization for institutional and mission customers. These systems will arrive at the end of FY 2015 and production is planned by the spring of FY 2016.

97 MATHEMATICS AND COMPUTING↗