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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Chapter 17: Residential Behavior Evaluation Protocol. The Uniform Methods Project: Methods for Determining Energy Efficiency Savings for Specific Measures, September 2011 - August 2020

This document has been updated in August 2020. This document was developed for the U.S. Department of Energy Uniform Methods Project (UMP). The UMP provides model protocols for determining energy and demand savings that result from specific energy-efficiency measures implemented through state and utility programs. In most cases, the measure protocols are based on a particular option identified by the International Performance Verification and Measurement Protocol; however, this work provides a more detailed approach to implementing that option. Each chapter is written by technical experts in collaboration with their peers, reviewed by industry experts, and subject to public review and comment. The protocols are updated on an as-needed basis. The UMP protocols can be used by utilities, program administrators, public utility commissions,evaluators, and other stakeholders for both program planning and evaluation. To learn more about the UMP, visit the website, https://energy.gov/eere/about-us/ump-home, or download the UMP introduction document at http://www.nrel.gov/docs/fy17osti/68557.pdf.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Chapter 22: Compressed Air Evaluation Protocol. The Uniform Methods Project: Methods for Determining Energy Efficiency Savings for Specific Measures (September 2011 - August 2020)

Compressed-air systems are used widely throughout industry for many operations, including pneumatic tools, packaging and automation equipment, conveyors, and other industrial process operations. Compressed-air systems are defined as a group of subsystems composed of air compressors, air treatment equipment, controls, piping, pneumatic tools, pneumatically powered machinery, and process applications using compressed air. A compressed-air system has three primary functional subsystems: supply, distribution, and demand. Air compressors are the primary energy consumers in a compressed-air system and are the primary focus of this protocol. The two compressed-air energy efficiency measures specifically addressed in this protocol are: high-efficiency/variable speed drive (VSD) compressor replacing modulating, load/unload, or constant-speed compressor; compressed-air leak survey and repairs. This protocol provides direction on how to reliably verify savings from these two measures using a consistent approach for each.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1982

Nonlinear optimization with linear constraints using a projection method

Nonlinear optimization problems that are encountered in science and industry are examined. A method of projecting the gradient vector onto a set of linear contraints is developed, and a program that uses this method is presented. The algorithm that generates this projection matrix is based on the Gram-Schmidt method and overcomes some of the objections to the Rosen projection method.

Fox, T.↗
NASA NTRS1998

An Assessment of Artificial Compressibility and Pressure Projection Methods for Incompressible Flow Simulations

Performance of the two commonly used numerical procedures, one based on artificial compressibility method and the other pressure projection method, are compared. These formulations are selected primarily because they are designed for three-dimensional applications. The computational procedures are compared by obtaining steady state solutions of a wake vortex and unsteady solutions of a curved duct flow. For steady computations, artificial compressibility was very efficient in terms of computing time and robustness. For an unsteady flow which requires small physical time step, pressure projection method was found to be computationally more efficient than an artificial compressibility method. This comparison is intended to give some basis for selecting a method or a flow solution code for large three-dimensional applications where computing resources become a critical issue.

Kwak, Dochan↗
NASA NTRS1989

Projection methods for the numerical solution of Markov chain models

Projection methods for computing stationary probability distributions for Markov chain models are presented. A general projection method is a method which seeks an approximation from a subspace of small dimension to the original problem. Thus, the original matrix problem of size N is approximated by one of dimension m, typically much smaller than N. A particularly successful class of methods based on this principle is that of Krylov subspace methods which utilize subspaces of the form span(v,av,...,A(exp m-1)v). These methods are effective in solving linear systems and eigenvalue problems (Lanczos, Arnoldi,...) as well as nonlinear equations. They can be combined with more traditional iterative methods such as successive overrelaxation, symmetric successive overrelaxation, or with incomplete factorization methods to enhance convergence.

Saad, Youcef↗
DOE OSTI2026

A projection method for particle resampling

Particle discretizations of partial differential equations are advantageous for high-dimensional kinetic models in phase-space due to their better scalability than continuum approaches with respect to dimension. Complex processes collectively referred to as particle noise hamper long time simulations with particle methods. One approach to address this problem is particle mesh adaptivity, or remapping, known as particle resampling and remeshing. Here, this work introduces a resampling method that projects particles to and from a (finite element) function space. The method is simple, using standard sparse linear algebra and finite element techniques, and it preserves all moments up to the order of a polynomial represented exactly by the continuum function space. It is distinguished from most other mesh-based methods in that new particle positions and number are decoupled from the mesh, allowing particle and continuum meshes to be adapted relatively independently. While this work is developed with structured particle and continuum phase-space grids on 1X + 1V Vlasov-Poisson models of Landau damping and two-stream instability, the method is well-suited to unstructured grids. Stable long time dynamics are demonstrated up to time T = 500. Reproducibility artifacts and data are publicly available.

Kinetic methods↗
NASA NTRSDate not supplied

Mode Projection Method for Target Mode Identification

A method for projecting flight configuration eigenvectors onto the vector space of the test configuration eigenvectors is proposed. The underlying concept of the proposed method is that any flight configuration eigenvectors that can be replicated by a linear combination of test configuration eigenvectors, is correlated, if the constituent test eigenvectors are themselves correlated. Therefore, the modal test target mode shapes should be those test modes that combine to form the important modes of the flight configurations. This approach also recognizes that it is the deformed shapes that dictate what sections of the structure are “highly strained” (in a relative sense, within each mode shape), and that it is these highly strained sections that require an accurate stiffness representation to achieve an accurate model correlation.

Modal Testing↗
NASA NTRSDate not supplied

Mode Projection Method for Target Mode Identification

A method for projecting flight configuration eigenvectors onto the vector space of the test configuration eigenvectors is proposed. The underlying concept of the proposed method is that any flight configuration eigenvectors (that can be replicated by a linear combination of test configuration eigenvectors) is correlated if the constituent test eigenvectors are themselves correlated. Therefore, the modal test target mode shapes should be those test modes that combine to form the important modes of the flight configurations. This approach also recognizes that it is the deformed shapes that dictate what sections of the structure are “highly strained” (in a relative sense, within each mode shape), and that these “highly strained” sections require an accurate stiffness representation to achieve an accurate model correlation.

Jeffrey A Peck↗
DOE OSTI2026

Uniform Methods Project: History and Updates [Slides]

This presentation provides an overview of the 2026 update effort and summarizes the drivers and history of the Uniform Methods Project (UMP). The presentation will be used in a public webinar to facilitate stakeholder participation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1993

A multilevel adaptive projection method for unsteady incompressible flow

There are two main requirements for practical simulation of unsteady flow at high Reynolds number: the algorithm must accurately propagate discontinuous flow fields without excessive artificial viscosity, and it must have some adaptive capability to concentrate computational effort where it is most needed. We satisfy the first of these requirements with a second-order Godunov method similar to those used for high-speed flows with shocks, and the second with a grid-based refinement scheme which avoids some of the drawbacks associated with unstructured meshes. These two features of our algorithm place certain constraints on the projection method used to enforce incompressibility. Velocities are cell-based, leading to a Laplacian stencil for the projection which decouples adjacent grid points. We discuss features of the multigrid and multilevel iteration schemes required for solution of the resulting decoupled problem. Variable-density flows require use of a modified projection operator--we have found a multigrid method for this modified projection that successfully handles density jumps of thousands to one. Numerical results are shown for the 2D adaptive and 3D variable-density algorithms.

Howell, Louis H.↗
DOE OSTI2020

Eulerian thermo-mechanical simulations of heterogeneous solid propellants using an approximate projection method

We present a mathematical model that describes thermo-mechanical deformations and thermal gradients on the unsteady burning of a heterogeneous solid propellant. A scaling study shows that the deformations in solid at combustion timescales can be treated as quasi-static. The resulting thermo-mechanical formulation is formulated on a Cartesian grid and makes use of a weak form of Chorin-type projection method to deal with large difference in shear modulus of constituent materials. Further, a one-dimensional verification study is carried out by comparing numerical simulations with those of an analytical model. In addition, convergence studies for a two-dimensional propellant sandwich configuration are presented for the stress, velocity, and reference map components. Finally, simulations are carried out for a two-dimensional random propellant pack and the time-averaged burn rate and strains are reported.

42 ENGINEERING↗
DOE OSTI2023

The Uniform Methods Project: Smart Thermostat Evaluation Protocol

A smart thermostat is an internet-connected device that controls home heating, ventilation, and air-conditioning (HVAC) equipment and can automatically adjust temperature set points to optimize performance and achieve energy savings. Smart thermostat features often include two way communication, occupancy detection (such as geofencing and occupancy sensors), schedule learning, and seasonal optimization algorithms. Smart thermostats can control most conventional HVAC systems, including central air conditioners, heat pumps, and forced air furnaces. Several types of residential utility programs offer smart thermostats as replacements measures. Working with smart thermostat vendors, utilities can offer separate optimization programs to produce energy savings beyond those achieved by installing a smart thermostat. From an evaluation perspective, smart thermostat programs have several noteworthy features. First, the energy savings from a smart thermostat may change over the life of the device. As a smart thermostat is connected to the internet, original equipment manufacturers can update the thermostat software to improve the thermostat's energy efficiency. Likewise, users can adjust the thermostat settings and schedules over time in response to changes in weather, thermal comfort, energy prices, or preferences for energy efficiency. Additionally, many thermostat manufacturers offer seasonal optimization programs that recommend changes or make minor, automated adjustments to the thermostat settings to improve energy efficiency. These opt-in programs are now standard offerings for many smart thermostat manufacturers and provided at no additional cost to users. The potential for software updates and continuous optimization and the evolving nature of user interactions mean future energy savings may differ from first-year savings and the energy savings of smart thermostats may need to be evaluated more than once. Second, smart thermostats often have small unit energy savings relative to a home's total energy consumption, especially in comparison to whole- home retrofit programs. This can make it difficult to detect the smart thermostat savings in billing or advanced metering infrastructure (AMI) meter consumption data. For example, as cooling loads in many regions average about 20% of annual electricity consumption, smart thermostat savings of 10% of cooling energy use would equate to a 2% reduction in home electricity consumption. Evaluators should use regression analysis of whole-home billing consumption or advanced metering infrastructure (AMI) meter consumption data to evaluate smart thermostat savings because, as explained at greater length below , these data are usually available to evaluators and regression can control for the impacts of weather and other potentially confounding factors on a home's energy consumption. Finally, as with other energy efficiency programs, participation in smart thermostat programs is self-selective. As discussed at greater length below , smart thermostat participants tend to be, among other things, younger, higher-income, and more likely to adopt electric vehicles (EVs) and internet connected devices than nonparticipants. These differences are often unobservable to the evaluator and correlated with a home's energy consumption, creating the potential for bias in estimating savings. Due to the small unit savings of thermostats, errors and biases from self-selection that may not be very consequential when evaluating a whole- home retrofits (e.g., ±2% of home electricity consumption) can have a major impact when evaluating the savings and cost-effectiveness of smart thermostat programs. A percentage point change in the estimated savings could affect the cost-effectiveness of a program. This means it is important for evaluators to assess and to minimize the potential for error from selection bias in estimating smart thermostat program savings. The Uniform Methods Project provides model protocols for determining energy savings and demand reductions that result from specific energy efficiency measures implemented through state and utility programs. In most cases, the measure protocols are based on a particular option identified by the International Performance Verification and Measurement Protocol ; however, this work provides a more detailed approach to implementing that option. Each chapter is written by technical experts in collaboration with their peers, reviewed by industry experts, and subject to public review and comment. The UMP protocols can be used by utilities, program administrators, public utility commissions, evaluators, and other stakeholders for both program planning and evaluation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Highly Resolved Reference Projections of Building Energy Use for the Contiguous United States: Building Sector Energy Baselines, Projection Methods, and Results

This report describes one methodology of projecting energy consumption of the US residential and commercial building sectors using NREL's ResStock™ and ComStock™ as well as growth rates derived from EIA's Annual Energy Outlook (AEO). The impetus for this work is to provide an intermediate method for compiling demand-side sectoral energy projections that is suitable for grid-scale analysis, such as NREL's Standard Scenarios. ResStock and ComStock are physics-based and statistically representative building stock models of the US residential and commercial sector, respectively. Using the 2012 actual meteorological year (AMY) weather data, the sectoral energy baselines are simulated and then segmented along key dimensions (e.g., geography, dwelling/building type). The segmented results are then scaled using the corresponding annual growth rates derived from the 2021 AEO reference case to produce energy projections out to 2050. The compiled result is a demand-side grid model (dsgrid) data set suitable for use in NREL's large-scale grid models, such as the Regional Energy Deployment System (ReEDS). This simple projection method does not endogenously represent how the building stock could evolve through time. Most notably, it does not reflect large-scale electrification, for example, the conversion of space heating, water heating, clothes drying, and cooking from primary fossil fuels to electricity, as this is not part of AEO's reference case assumptions. Nonetheless this approach is more resolved and potentially extensible compared to the current method used by Standard Scenarios's reference case, which augments a sector's total load based on a single growth rate from AEO.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Preliminary Analysis of Source Physics Experiment Explosion-Triggered Microseismicity Using the Back-Projection Method

A series of four chemical explosions were detonated in a deep borehole within the Yucca Flat Dry Alluvium Geology (DAG) at the Nevada National Security Site between 2018 and 2019. The two larger chemical explosions of 50 tons (DAG-2) and 10 tons (DAG-4) TNT equivalent yield triggered energetic aftershock sequences numbering 1392 and 347 microearthquakes, respectively, within the first 10 days. No significant aftershock activity was observed for the two smaller 1-ton explosions (DAG-1 and DAG-3). Here, we used a back-projection method based on travel-time migration and stacking of signal-to-noise ratio traces to detect, associate and locate aftershocks from a subset of 22-geophones within a larger 2 urn:x-wiley:21699313:media:jgrb54910:jgrb54910-math-0001 2 km seismic array surrounding the borehole. The aftershocks located within 300 m of the borehole and the depths were above the working points of 300 and 50 m depths of DAG-2 and DAG-4, respectively, ruling out triggering slip on geologic faults or disturbances beneath neighboring collapse craters. DAG-2 and DAG-4 aftershocks decayed at similar rates, with power-law exponents of p = 1.48 and p = 1.49, respectively. These decay rates are comparable to aftershocks sequences triggered by earthquakes and historical nuclear explosions at Yucca Flat. A smooth power-law aftershock decay within the first 10 days suggests a triggering mechanism from explosion generated stress relaxation due to the diffusion of high gas pressures in the cavity and radial fractures. A more random and episodic aftershock rate would be expected due to cavity collapse or falling rubble in chimney formation.

58 GEOSCIENCES↗
DOE OSTI2020

Project Methods to Enhance Wellbore Cement Integrity with Microbially-Induced Calcite Precipitation (MICP) (Final Scientific/Technical Report)

The goal of this project was to develop improved methods for sealing compromised wellbore cement in leaking oil and gas wells, thereby reducing the risk of unwanted upward fluid migration. Novel methods for improving wellbore integrity, such as microbially induced calcite precipitation (MICP), can reduce leakage potential, improve the safety of fossil fuel extraction, improve the public perception of hydraulic fracturing, and promote environmentally-prudent unconventional oil and gas development. Microbes, with the urease enzyme, can catalyze the chemical reaction of urea hydrolysis to induce the precipitation of calcium carbonate which can be used as a cementitious material to seal leakage pathways. In this project, methods to promote robust bio-composite cementitious materials were designed and tested in the laboratory. Scale-up of those methods were tested in meso-scale reactor systems and in field applications. In this report, in Section One, we describe laboratory efforts to develop injection strategies to promote precipitation in wellbore analogs and determine the strength of the bio-composite cements as compared to fine cement. In Section Two, we describe the efforts to scale up the work and study the use of materials that can be used in field application, for example exploring the use of calcium chloride ice melt or urea fertilizer as source chemicals. In Section Three, the three field trials (methods and results) performed as part of the project are described and summarized. At the end of the report is a comprehensive summary and conclusion section which highlights the key findings of the project. The work performed during this project significantly advanced the technology readiness level (TRL) of the MICP wellbore sealing strategy.

02 PETROLEUM↗
NASA NTRS2007

Projection Method for Flows with Large Density Variations

Numerical models of solidification including a mushy zone are notoriously inefficient; most of them are based on formulations that require the coupled solution of the velocity components in the momentum equation greatly restricting the range of applicability of the models. There are only two models known to the authors that have used a projection or fractional step formulation, but none of these were used to model problems of any significant size. A third model was only applied to a partial mushy zone with no all-fluid region. Our initial attempts at modeling directional solidification in the presence of a developing mushy zone using a projection formulation encountered very serious difficulties once solidification starts. These difficulties were traced to the inability of the method to deal with large local density differences in the vicinity of the fluid-mush interface. As a result, a modified formulation of the projection method has been developed, that maintains the coupling between the body force and the pressure gradient and is presented in this work. The new formulation is shown to be robust and efficient, and can be applied to problems involving very large meshes. This is illustrated in this work through its application to simulations involving Pb-Sb and Pb-Sn alloys.

Heinrich, Juan C.↗
NASA NTRS1990

Implementation of two projection methods on a shared memory multiprocessor - DEC VAX 6240

The relative performance of two iterative schemes, based on projection techniques, is compared on a shared memory multiprocessor - VAX 6240. The CG accelerated Block-SSOR method and the CG accelerated Symmetric-Kaczmarz method are considered for the solution of large sparse nonsymmetric systems of linear equations. It is shown that the regular structure of many matrices can be exploited by the CG-accelerated Block-SSOR method to provide good speedup in a multiprocessing environment. However, the CG accelerated Symmetric-Kaczmarz method, while being a viable alternative on a scalar machine, is unable to benefit from multiprocessing.

Kamath, C.↗
NASA NTRS1989

Robust eigenstructure assignment by a projection method - Applications using multiple optimization criteria

A methodology for robust eigenstructure assignment for multivariable feedback systems is presented. The algorithm is based upon a pole placement technique using projections onto subspaces of admissible eigenvectors. New ideas are introduced to generate target (desired) sets of unitary eigenvectors and determine optimal feasible eigenvectors in a least-square sense. Useful connections are established between the pole-placement by independent modal space control and the method introduced in this paper. A multicriterion optimization algorithm is also presented, which takes efficient advantage of the present eigenstructure assignment method. These developments show significant improvement over an earlier version of this algorithm in both computational cost and accuracy. This optimization process appears to be numerically robust and suitable for high-dimensional multicriterion optimizations; it is especially attractive for computer-aided design of control systems.

Rew, D. W.↗