Search NASA⌕ Search

SEARCH · Search NASA

Results for “Flexible Research Platform”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Evaluation of Variable Refrigerant Flow Systems Performance and the Enhanced Control Algorithm on Oak Ridge National Laboratory's Flexible Research Platform

A research project “Evaluation of Variable Refrigerant Flow (VRF) Systems Performance and the Enhanced Control Algorithm on Oak Ridge National Laboratory’s (ORNL’s) Flexible Research Platform” was performed to (1) install and validate the performance of Samsung VRF systems compared with the baseline rooftop unit (RTU) variable-air-volume (VAV) system and (2) evaluate the enhanced control algorithm for the VRF system on the two-story flexible research platform (FRP) in Oak Ridge, Tennessee. Based on the VRF system designed by Samsung and ORNL, the system was installed from February 18 through April 15, 2014. The final commissioning and system optimization were completed on June 2, 2014, and the initial test for system operation was started the following day, June 3, 2014. In addition, the enhanced control algorithm was implemented and updated on June 18. After a series of additional commissioning actions, the energy performance data from the RTU and the VRF system were monitored from July 7, 2014, through February 28, 2015. Data monitoring and analysis were performed for the cooling season and heating season separately, and the calibrated simulation model was developed and used to estimate the energy performance of the RTU and VRF systems. This final report includes discussion of the design and installation of the VRF system, the data monitoring and analysis plan, the cooling season and heating season data analysis, and the building energy modeling study

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Surrogate Model of Flexible Research Platform EnergyPlus Models to Enable Sensitivity Analysis

This letter report describes the surrogate models developed from the EnergyPlus model of Oak Ridge National Laboratory’s Flexible Research Platform. Two data-driven black-box models were developed, and the outputs of the surrogate models were compared with the EnergyPlus model. The two models developed are a multilayer perceptron deep learning model, and a long short-term memory (LSTM) neural network model. The three factors for selecting the black-box models are scalability, computation time, and accuracy. A total of 107 input variables were the dominant variables in determining the outputs of building energy consumptions and thermal comfort. A total of 54 output variables were identified as the prediction targets, including the system- and zone-level outputs. The large set of the simulation cases were generated by integrating sensor errors into an emulator based on EnergyPlus and Python EMS, which includes advanced control sequences from ASHRAE Guideline 36-2018: High-Performance Sequences of Operation. The surrogate models were developed based on a set of large-scale simulation runs (i.e., 4,000 runs) on a cloud platform. The comparison analysis shows that the two black-box models had good accuracy for predicting new outputs for sensitivity analysis using the root mean square error metric. As a next step, the developed surrogate models will be used to perform sensitivity analysis for different sensor impacts (e.g., sensor types, sensor locations).

42 ENGINEERING↗
DOE OSTI2022

Implementation of ASHRAE Guideline 36 Control Logic into Oak Ridge National Laboratory (ORNL)’s Flexible Research Platform (FRP)

This report presents the initial field test procedure and results for ASHRAE Guideline 36 control logic. The US Department of Energy’s Oak Ridge National Laboratory’s (ORNL’s) two-story Flexible Research Platform (FRP-2) was selected as a test building. The rooftop unit (RTU) system serves as the building’s primary cooling system, and each variable air volume (VAV) box with an electric reheat coil serves as the building’s heating system along with RTU gas furnace. In this study, the control for the RTU and VAV box adopted the practical control sequences from ASHRAE Guideline 36-2018: High-Performance Sequences of Operation. Each story of the FRP-2 building has 5 office zones (thermal zones), for a total of 10 zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Evaluation of Variable Refrigerant Flow Systems Performance on Oak Ridge National Laboratory s Flexible Research Platform: Part 1 Cooling Season Analysis

This report provides second-year cooling season test results for the multi-year project titled “Evaluation of Variable Refrigeration Flow (VRF) System on Oak Ridge National Laboratory (ORNL)’s Flexible Research Platform (FRP).” The purpose of the second-year project was to (1) evaluate the full- and partload performance of VRF systems compared with an existing baseline heating, ventilation, and airconditioning (HVAC) system, which is a conventional rooftop unit (RTU) variable-air-volume (VAV) system with electric resistance heating and (2) use hourly building energy simulation to evaluate the energy savings potential of using VRF systems in major US cities. The second-year project performance period was from July 2015 through June 2016.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Comparison Evaluations of VRF and RTU Systems Performance on Flexible Research Platform

The energy performance of a variable refrigerant flow (VRF) system was evaluated using an occupancy-emulated research building in the southeastern region of the United States. Full- and part-load performance of the VRF system in heating and cooling seasons was compared with a conventional rooftop unit (RTU) variable-air-volume system with electric resistance heating. During both the heating and cooling seasons, full- and part-load conditions (i.e., 100%, 75%, and 50% thermal loads) were maintained alternately for 2 to 3 days each, and the energy use, thermal conditions, and coefficient of performance (COP) for the RTU and VRF system were measured. During the cooling season, the VRF system had an average COP of 4.2, 3.9, and 3.7 compared with 3.1, 3.0, and 2.5 for the RTU system under 100%, 75%, and 50% load conditions and resulted in estimated energy savings of 30%, 37%, and 47%, respectively. Finally, during the heating season, the VRF system had an average COP ranging from 1.2 to 2.0, substantially higher than the COPs of the RTU system, and resulted in estimated energy savings of 51%, 47%, and 27% under the three load conditions, respectively.

42 ENGINEERING↗
DOE OSTI2022

Dataset from ORNL Flexible Research Platform (FRP)

The data comes from a two-story light commercial building which can be used to physically simulate light commercial buildings common in the nation's existing building stock. Based on a data collection plan, these measurements were performed for five different building operations. In the data set for each period, 98 data variables were selected and collected, in which 7 variables are weather data and the rest are all building and system operation data.

Cui, Borui↗
DOE OSTI2016

Multiyear Plan for Validation of EnergyPlus Multi-Zone HVAC System Modeling using ORNL's Flexible Research Platform

This document describes the Oak Ridge National Laboratory (ORNL) multiyear experimental plan for validation and uncertainty characterization of whole-building energy simulation for a multi-zone research facility using a traditional rooftop unit (RTU) as a baseline heating, ventilating, and air conditioning (HVAC) system. The project’s overarching objective is to increase the accuracy of energy simulation tools by enabling empirical validation of key inputs and algorithms. Doing so is required to inform the design of increasingly integrated building systems and to enable accountability for performance gaps between design and operation of a building. The project will produce documented data sets that can be used to validate key functionality in different energy simulation tools and to identify errors and inadequate assumptions in simulation engines so that developers can correct them. ASHRAE Standard 140, Method of Test for the Evaluation of Building Energy Analysis Computer Programs (ASHRAE 2004), currently consists primarily of tests to compare different simulation programs with one another. This project will generate sets of measured data to enable empirical validation, incorporate these test data sets in an extended version of Standard 140, and apply these tests to the Department of Energy’s (DOE) EnergyPlus software (EnergyPlus 2016) to initiate the correction of any significant deficiencies. The fitness-for-purpose of the key algorithms in EnergyPlus will be established and demonstrated, and vendors of other simulation programs will be able to demonstrate the validity of their products. The data set will be equally applicable to validation of other simulation engines as well.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Evaluation of Variable Refrigerant Flow Systems Performance on Oak Ridge National Laboratory s Flexible Research Platform: Part 2 Heating Season Analysis

The performance of two HVAC systems was evaluated using ORNL’s FRP, which is a two-story, 3,200 ft2 (297.3 m2) multi-zone unoccupied building that represents a typical low-rise, small office building common in the US existing building stock. The FRP is equipped with a conventional 12.5 ton (44 kW) RTU-VAV reheat system as the baseline system. For this study, a 12 ton (42 kW) VRF with a dedicated outdoor air system (DOAS) was installed to be compared with the baseline RTU system.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Empirical Validation of Building Energy Modeling using Flexible Research Platform

Recently, efforts have been made to provide reliable empirical data for ANSI/ASHRAE Standard 140, Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs, to enable improved accuracy of building energy model (BEM) engines and improved characterization of their accuracy. The motivation for this effort is that the use of reliable empirical validation data sets in the evaluation of building energy modeling tools will lead to more consistent and validated simulation engines across all software vendors. This would expedite the use of building energy modeling in designing new buildings and retrofitting existing buildings, which delivers more energy efficient buildings.As part of a three-year multi-lab empirical validation project sponsored by U.S. DOE, this research project generated cooling season test plans by reviewing ASHRAE Standard 140, and the tests were performed based on the test plan. Finally, the experimental data sets were compared with the EnergyPlus model to demonstrate the validation procedure.

Im, Piljae↗
NASA NTRS2012

Autonomous Operations Planner: A Flexible Platform for Research in Flight-Deck Support for Airborne Self-Separation

The Autonomous Operations Planner (AOP), developed by NASA, is a flexible and powerful prototype of a flight-deck automation system to support self-separation of aircraft. The AOP incorporates a variety of algorithms to detect and resolve conflicts between the trajectories of its own aircraft and traffic aircraft while meeting route constraints such as required times of arrival and avoiding airspace hazards such as convective weather and restricted airspace. This integrated suite of algorithms provides flight crew support for strategic and tactical conflict resolutions and conflict-free trajectory planning while en route. The AOP has supported an extensive set of experiments covering various conditions and variations on the self-separation concept, yielding insight into the system s design and resolving various challenges encountered in the exploration of the concept. The design of the AOP will enable it to continue to evolve and support experimentation as the self-separation concept is refined.

Karr, David A.↗
DOE OSTI2022

Preliminary Sensitivity Analysis for Sensors Impacts on Building Control Performance

This report describes the preliminary sensitivity analysis for sensor impacts on building control performance through the US Department of Energy’s Oak Ridge National Laboratory’s Flexible Research Platform (FRP-2) building. The rooftop unit system provides cooling and heating to the building. The main heating coil is a gas heating coil. Each zone is served by a variable air volume box with an electricity reheat coil. The rooftop unit and variable air volume box controls adopted the practical control sequences from ASHRAE Guideline 36-2018: High-Performance Sequences of Operation. For sensors, the incipient (time-changing) sensor errors, including bias sensor error and precision sensor error, are the inputs of interest. The outputs are energy consumption and thermal comfort (e.g., the predicted percentage of dissatisfied occupants). The large-scale simulation (3,600 cases) was conducted on a cloud platform by integrating sensor errors and ASHRAE Guideline 36 control sequences into an emulator based on the EnergyPlus simulation program with Python energy management system feature. The surrogate models were developed based on cloud simulation results. The uncertainty analysis showed that the sensor errors substantially affect building energy consumption and thermal comfort. The sensitivity analysis shows a ranking of sensor error impacts for each interested output item (e.g., cooling energy, reheat coil heating energy, predicted percentage of dissatisfied occupants). In FY 2022, sensor locations, types, and costs will be evaluated. The field test in Oak Ridge National Laboratory’s Flexible Research Platform building regarding sensor impacts will also be performed. Finally, a comparative analysis will be conducted based on the field test results and emulator results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Evaluate the impact of sensor accuracy on model performance in data-driven building fault detection and diagnostics using Monte Carlo simulation

The performance of data-driven fault detection and diagnostics (FDD) is heavily dependent on sensors. However, sensor inaccuracy and sensor faults are pervasive in building operation: inaccurate and missing sensor readings deteriorate FDD performance; sensor inaccuracy will also affect the selection of sensor for data-driven FDD in the model training process, which is another key factor of data-driven FDD performance. Sensor accuracy and sensor selection individually are well-studied research topics in this field, but the impact of sensor accuracy on sensor selection and its further impact on FDD performance has not been evaluated and quantified. In this paper, we developed a novel analysis methodology that comprehensively evaluates sensor fault on sensor selection and FDD accuracy. Monte Carlo simulation is applied to deal with multiple stochastic sensor inaccuracy and provide probabilistic analysis results of the impact of sensor inaccuracy on sensor selection and FDD accuracy. This methodology focuses on the net impact of fault states across a full sensor set. The developed methodology can be used for the early-stage sensor design and operation-stage sensor maintenance. Furthermore, a case study is conducted to demonstrate the analysis methodology using a commercial building model crated to Flexible Research Platform located at Oak Ridge National Laboratory, USA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A systematic feature extraction and selection framework for data-driven whole-building automated fault detection and diagnostics in commercial buildings

In data-driven automated fault detection and diagnostics (AFDD) modeling for building energy systems, feature engineering is a critical process of extracting information from high-dimensional and noisy sensor measurement and turning it into informative and representative inputs or features for data-driven modeling. However, few studies specifically discuss the feature engineering, especially the interactions between feature extraction and feature selection in whole-building AFDD. We developed a systematic feature extraction and selection framework for whole-building AFDD. In this framework, features are aggressively extracted from raw sensor data using statistical feature extraction techniques with various window sizes and statistics. With many features extracted, a hybrid feature selection algorithm that combines the filter and wrapper method then selects the best feature set. The framework considers diversity in the duration of fault behavior among fault types in whole-building AFDD, thus achieving high model generalization. We implemented our developed framework in a virtual testbed calibrated with measured data from Oak Ridge National Laboratory's Flexible Research Platform designed to mimic the operation of a typical small commercial building. The AFDD model is trained by the simulation data generated from the virtual testbed. The results show that (1) the developed framework improves the generalization of the AFDD model by 10.7% compared with literature-reported feature extraction and selection methods and (2) features with diverse window sizes and statistics are selected, providing insight into physical systems beyond the current understanding of buildings and faults and improving the detection and diagnostics of multiple fault types.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Experimental test of model predictive control in a variable air volume system

Model predictive control (MPC) has been widely studied as a promising approach for improving energy efficiency and operational flexibility in buildings, yet its real-world performance for commercial variable air volume (VAV) systems remains insufficiently characterized. In particular, the impacts of model mismatch on control robustness, real-time computational burden, and device-level operation are rarely evaluated using long-term field data. Here, this study presents a comprehensive experimental evaluation of MPC applied to a full-scale VAV system in Oak Ridge National Laboratory’s Flexible Research Platform-2 building with constant cooling/heating temperature setpoints and no occupancy. The study offers three key advantages over existing work: (1) it uses a representative building in a full-scale experimental test, capturing realistic system dynamics and complexity; (2) it evaluates a relatively sophisticated MPC formulation using two different optimization solvers (Gurobi and PSO), fully accounting for computational complexity and methodological diversity; and (3) it systematically assesses potential negative impacts on various building devices, benchmark against a well-established baseline, ASHRAE Guideline 36 (G36). To isolate zone- and air-handling-unit–level supervisory control effects, the supply fan was operated with a fixed static pressure setpoint under all strategies, and the trim-and-response static pressure reset in G36 was not enabled. Results show that MPC maintained thermal comfort while improving energy efficiency. Abrupt solar radiation variations degraded performance. Computation times ranged from ∼1 s (Gurobi) to ∼ 70 s (PSO). Compared with G36, MPC achieves 33% energy savings and reduces median reheat coil output by approximately a factor of 5–10 for a representative cooling day under matched weather conditions. However, it increases the maximum discomfort deviation from 0.5 to 1°C and results in a 32% increase in staging frequency. In addition, PSO-based MPC introduced damper oscillations, also affecting actuator longevity.

ASHRAE guideline 36↗
DOE OSTI2022

Sensor cost-effectiveness analysis for data-driven fault detection and diagnostics in commercial buildings

Data-driven building fault detection and diagnostics (FDD) is heavily dependent on sensors. However, common sensors from Building Automation Systems are not optimized to maximize accuracy in FDD. Installing additional sensors that provide more detailed building system information is key to maximizing the performance of FDD solutions. Here in this paper, we present a sensor cost analysis workflow to quantify the economic implications of installing new sensors for FDD using the concept of sensor threshold marginal cost (STMC). STMC does not represent actual sensor cost. Rather, it represents a target cost based on the economic benefit that would be realized through improved FDD performance and one or more specified economic criteria. We calculate STMCs for multiple possible fault types and use fault prevalence information to aggregate STMCs into a single dollar value to determine the cost-effectiveness of a potential sensor investment. We conducted a case study using Oak Ridge National Laboratory's Flexible Research Platform (FRP) test facility as a reference. The case study demonstrates the feasibility of the analysis and highlights the key cost considerations in sensor selection for FDD. The results also indicate that identifying and installing the few key sensor(s) is critical to cost-effectively improve FDD performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Grey-box and ANN-based building models for multistep-ahead prediction of indoor temperature to implement model predictive control

Model-based predictive control (MPC) strategies for heating, ventilation, and air-conditioning (HVAC) systems present an opportunity to lower building energy consumption and operational costs. Such approaches rely on the development of a model to precisely forecast building thermal dynamics, such as room air temperature or heating/cooling rate, and make control-related decisions. The control-oriented modeling of building energy systems should be accurate in predicting indoor conditions and present low computational complexity. These features are the key challenge of implementing advanced control methods such as MPC. Extant studies on building modeling for MPC have focused on step-ahead forecasting techniques to forecast building thermal dynamics, while multistep-ahead forecasting is essential. Moreover, machine learning model suitable in case of the domain-based engineering expertise are also not available. To this aim, we perform a comparative analysis of the grey-box model based on a resistance-capacitance (RC) thermal network and a machine learning model composed of an artificial neural network (ANN) for multistep-ahead prediction of building thermal dynamics using current and historical data. Actual experimental data obtained from the Flexible Research Platform (FRP) in Oak Ridge National Laboratory (US) are used for estimation and validation purposes. The average root mean squared error (RMSE) of the grey-box and ANN models are 0.89 °C and 1.02°C, respectively. Finally, the results indicate that the grey-box model outperforms the ANN model in the considered validation periods in terms of accuracy and prediction stability.

42 ENGINEERING↗