Search NASA⌕ Search

Engineering topics

Brackney, Larry

Publications and source records attributed to Brackney, Larry.

Alfalfa [SWR 18-17]

Alfalfa is a web based software service that implements a virtual building control system using the EnergyPlus simulation as a backend. Conventional building energy simulation is non interactive, meaning that a simulation is started and run to completion without any user input during the simulation. Alfalfa allows a client to start a building simulation, then interactively read sensor locations and dynamically command actuators in realtime. The Alfalfa web service conforms to the Project Haystack standard, which is a specification adopted by many building control system vendors. The service enables Haystack compliant control system vendors to test their control modules and algorithms without the need for a physical building or modifying their hardware and software, enabling faster and cheaper development cycles. Alfalfa has use cases beyond control system testing. One example is building fault detection, where physical building performance can be compared to simulated values in realtime, indicating where there may be a problem in the building. Another use case is informing predictive control systems, where near future events are simulated to premptively guide control decisions in the real building. This software is web based and designed to be self service. Users of Alfalfa can upload an OpenStudio building model and begin communicating with the simulated building without any advanced setup or configuration, as if they were communicating a real building control system. Behind the scenes, Alfalfa makes extensive use of Amazon Web Services, in order to scale from a very small number of users and buildings, to extremely large numbers in a cost effective way.

Benne, Kyle↗

Making Commercial Whole Building Program Delivery Easier and Less Risky Greg Thomas, Performance Systems Development

Whole building commercial programs can be challenging to deliver due to their complexity and scope. A broad suite of Federal tools and reference standards can be leveraged to reduce program cost and risk, and improve scalability. The OpenEfficiency Initiative (OEI) is a DoE funded effort to expand the deployment of Federal tools and reference standards into whole building commercial efficiency programs. OEI is developing an open source data exchange platform which is being informed and motivated by pilots.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

OpenStudio: A Platform for Ex Ante Incentive Programs

Many utilities operate programs that provide ex ante (up front) incentives for building energy conservation measures (ECMs). A typical incentive program covers two kinds of ECMs. ECMs that deliver similar savings in different contexts are associated with pre-calculated 'deemed' savings values. ECMs that deliver different savings in different contexts are evaluated on a 'custom' per-project basis. Incentive programs often operate at less than peak efficiency because both deemed ECMs and custom projects have lengthy and effort-intensive review processes--deemed ECMs to gain confidence that they are sufficiently context insensitive, custom projects to ensure that savings are claimed appropriately. DOE's OpenStudio platform can be used to automate ex ante processes and help utilities operate programs more efficiently, consistently, and transparently, resulting in greater project throughput and energy savings. A key concept of the platform is the OpenStudio Measure, a script that queries and transforms building energy models. Measures can be simple or surgical, e.g., applying different transformations based on space-type, orientation, etc. Measures represent ECMs explicitly and are easier to review than ECMs that are represented implicitly as the difference between a with-ECM and without-ECM models. Measures can be automatically applied to large numbers of prototype models--and instantiated from uncertainty distributions--facilitating the large scale analysis required to develop deemed savings values. For custom projects, Measures can also be used to calibrate existing building models, to automatically create code baseline models, and to perform quality assurance screening.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Scout: An Impact Analysis Tool for Building Energy-Efficiency Technologies

Evaluating the national impacts of candidate U.S. building energy-efficiency technologies has historically been difficult for organizations with large energy efficiency portfolios. In particular, normalizing results from technology-specific impact studies is time-consuming when those studies do not use comparable assumptions about the underlying building stock. To equitably evaluate its technology research, development, and deployment portfolio, the U.S. Department of Energy's Building Technologies Office has developed Scout, a software tool that quantitatively assesses the energy and CO2 impacts of building energy-efficiency measures on the national building stock. Scout efficiency measures improve upon the unit performance and/or lifetime operational costs of an equipment stock baseline that is determined from the U.S. Energy Information Administration Annual Energy Outlook (AEO). Scout measures are characterized by a market entry and exit year, unit performance level, cost, and lifetime. To evaluate measures on a consistent basis, Scout uses EnergyPlus simulation on prototype building models to translate measure performance specifications to whole-building energy savings; these savings impacts are then extended to a national scale using floor area weighting factors. Scout represents evolution in the building stock over time using AEO projections for new construction, retrofit, and equipment replacements, and competes technologies within market segments under multiple adoption scenarios. Scout and its efficiency measures are open-source, as is the EnergyPlus whole building simulation framework that is used to evaluate measure performance. The program is currently under active development and will be formally released once an initial set of measures has been analyzed and reviewed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Building energy analysis tool

A building energy analysis system includes a building component library configured to store a plurality of building components, a modeling tool configured to access the building component library and create a building model of a building under analysis using building spatial data and using selected building components of the plurality of building components stored in the building component library, a building analysis engine configured to operate the building model and generate a baseline energy model of the building under analysis and further configured to apply one or more energy conservation measures to the baseline energy model in order to generate one or more corresponding optimized energy models, and a recommendation tool configured to assess the one or more optimized energy models against the baseline energy model and generate recommendations for substitute building components or modifications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Applications of active adaptive noise control to jet engines

During phase 2 research on the application of active noise control to jet engines, the development of multiple-input/multiple-output (MIMO) active adaptive noise control algorithms and acoustic/controls models for turbofan engines were considered. Specific goals for this research phase included: (1) implementation of a MIMO adaptive minimum variance active noise controller; and (2) turbofan engine model development. A minimum variance control law for adaptive active noise control has been developed, simulated, and implemented for single-input/single-output (SISO) systems. Since acoustic systems tend to be distributed, multiple sensors, and actuators are more appropriate. As such, the SISO minimum variance controller was extended to the MIMO case. Simulation and experimental results are presented. A state-space model of a simplified gas turbine engine is developed using the bond graph technique. The model retains important system behavior, yet is of low enough order to be useful for controller design. Expansion of the model to include multiple stages and spools is also discussed.

Shoureshi, Rahmat↗