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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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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Integrated simulation of U-10Mo monolithic fuel swelling behavior

Here, a separate computational branch has been implemented within the DART (Dispersion Analysis Research Tool) computational code to simulate the swelling behavior of U-10Mo monolithic fuel under the operating conditions of high-power research and test reactors (RTRs). The monolithic branch of the DART code implements a mechanistic rate-theory-based fission-gas-behavior model for the calculation of fission gas swelling, as well as a suite of thermal, physical, and mechanical models to take into account various processes occurring in RTR fuels during irradiation. In order to accurately simulate and eventually predict U-10Mo monolithic fuel irradiation behavior, the code uses materials properties calculated with lower length-scale computational methods, such as gas atom diffusivity and U-Mo surface energy from atomic simulations and grain-morphology-specific recrystallization kinetics (recrystallized fuel volume fractions vs. fission density) predicted using the phase-field method. The remainder of fission gas behavior parameters used in the model were calibrated with measured intergranular bubble size distributions. With this integrated simulation approach, the swelling behavior of U-10Mo monolithic fuel was simulated for various initial grain sizes at different operating conditions and compared with measured data. Furthermore, because limited experimental data exist for parameter calibration detailed sensitivity studies for the important parameters used in the fission gas behavior model were performed in order to examine their impact on both intergranular gas bubble morphology at low fission density, and on total porosity at high fission density.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2004

Cognitive Modeling of Social Behaviors

The driving theme of cognitive modeling for many decades has been that knowledge affects how and which goals are accomplished by an intelligent being (Newell 1991). But when one examines groups of people living and working together, one is forced to recognize that whose knowledge is called into play, at a particular time and location, directly affects what the group accomplishes. Indeed, constraints on participation, including roles, procedures, and norms, affect whether an individual is able to act at all (Lave & Wenger 1991; Jordan 1992; Scribner & Sachs 1991). To understand both individual cognition and collective activity, perhaps the greatest opportunity today is to integrate the cognitive modeling approach (which stresses how beliefs are formed and drive behavior) with social studies (which stress how relationships and informal practices drive behavior). The crucial insight is that norms are conceptualized in the individual &nd as ways of carrying out activities (Clancey 1997a, 2002b). This requires for the psychologist a shift from only modeling goals and tasks - why people do what they do - to modeling behavioral patterns-what people do-as they are engaged in purposeful activities. Instead of a model that exclusively deduces actions from goals, behaviors are also, if not primarily, driven by broader patterns of chronological and located activities (akin to scripts). This analysis is particular inspired by activity theory (Leont ev 1979). While acknowledging that knowledge (relating goals and operations) is fundamental for intelligent behavior, activity theory claims that a broader driver is the person s motives and conceptualization of activities. Such understanding of human interaction is normative (i.e., viewed with respect to social standards), affecting how knowledge is called into play and applied in practice. Put another way, how problems are discovered and framed, what methods are chosen, and indeed who even cares or has the authority to act, are all constrained by norms, which are conceived and enacted by individuals.

Clancey, William J.↗
DOE OSTI2022

Unifying Energy-Management Problems for Inverter-Based Power Networks

This paper outlines a suite of energy-management problems for inverter-based power networks from the vantage point of optimal control and (non)linear optimization. The problems are categorized based on timescales dictated by the network dynamics, and organized methodologically based on the problem complexity. A growing body of literature has addressed problems in this domain, albeit, with poorly motivated assumptions and behavioral models that obscure precise device behavior. With a combination of circuit- and control-theoretic lenses, we establish appropriate dynamic models for the networked resources, illustrate how common engineering assumptions arise, uncover how problems are linked, and postulate open challenges.

(non)linear optimization↗
NASA NTRS2010

Physics of Life: A Model for Non-Newtonian Properties of Living Systems

This innovation proposes the reconciliation of the evolution of life with the second law of thermodynamics via the introduction of the First Principle for modeling behavior of living systems. The structure of the model is quantum-inspired: it acquires the topology of the Madelung equation in which the quantum potential is replaced with the information potential. As a result, the model captures the most fundamental property of life: the progressive evolution; i.e. the ability to evolve from disorder to order without any external interference. The mathematical structure of the model can be obtained from the Newtonian equations of motion (representing the motor dynamics) coupled with the corresponding Liouville equation (representing the mental dynamics) via information forces. All these specific non-Newtonian properties equip the model with the levels of complexity that matches the complexity of life, and that makes the model applicable for description of behaviors of ecological, social, and economical systems. Rather than addressing the six aspects of life (organization, metabolism, growth, adaptation, response to stimuli, and reproduction), this work focuses only on biosignature ; i.e. the mechanical invariants of life, and in particular, the geometry and kinematics of behavior of living things. Living things obey the First Principles of Newtonian mechanics. One main objective of this model is to extend the First Principles of classical physics to include phenomenological behavior on living systems; to develop a new mathematical formalism within the framework of classical dynamics that would allow one to capture the specific properties of natural or artificial living systems such as formation of the collective mind based upon abstract images of the selves and non-selves; exploitation of this collective mind for communications and predictions of future expected characteristics of evolution; and for making decisions and implementing the corresponding corrections if the expected scenario is different from the originally planned one. This approach postulates that even a primitive living species possesses additional, non-Newtonian properties that are not included in the laws of Newtonian or statistical mechanics. These properties follow from a privileged ability of living systems to possess a self-image (a concept introduced in psychology) and to interact with it. The proposed mathematical system is based on the coupling of the classical dynamical system representing the motor dynamics with the corresponding Liouville equation describing the evolution of initial uncertainties in terms of the probability density and representing the mental dynamics. The coupling is implemented by the information-based supervising forces that can be associated with self-awareness. These forces fundamentally change the pattern of the probability evolution, and therefore, lead to a major departure of the behavior of living systems from the patterns of both Newtonian and statistical mechanics. This innovation is meant to capture the signature of life based only on observable behavior, not on any biochemistry. This will not prevent the use of this model for developing artificial living systems, as well as for studying some general properties of behavior of natural, living systems.

Zak, Michail↗
NASA NTRS2017

Modeling Pilot Pulse Control

In this study, behavioral models are developed that closely reproduced pulsive control response of two pilots from the experimental pool using markedly different control techniques (styles) while conducting a tracking task. An intriguing find was that the pilots appeared to: 1) produce a continuous, internally-generated stick signal that they integrated in time; 2) integrate the actual stick position; and 3) compare the two integrations to issue and cease pulse commands. This suggests that the pilots utilized kinesthetic feedback in order to perceive and integrate stick position, supporting the hypothesis that pilots can access and employ the proprioceptive inner feedback loop proposed by Hess' pilot Structural Model. The Pulse Models used in conjunction with the pilot Structural Model closely recreated the pilot data both in the frequency and time domains during closed-loop simulation. This indicates that for the range of tasks and control styles encountered, the models captured the fundamental mechanisms governing pulsive and control processes. The pilot Pulse Models give important insight for the amount of remnant (stick output uncorrelated with the forcing function) that arises from nonlinear pilot technique, and for the remaining remnant arising from different sources unrelated to tracking control (i.e. neuromuscular tremor, reallocation of cognitive resources, etc.).

workload↗
DOE OSTI2025

Machine learning-enabled discovery of ionic liquid–solvent electrolytes exhibiting high ionic conductivity

Ionic liquids (ILs), which are a class of materials with versatile nature and growing popularity, are facing impediments toward widespread usage as electrolytes due to various factors such as low ionic conductivity, high viscosity, high market price etc. One of the ways these limitations can be addressed is by mixing ILs with a molecular solvent. In a combinatorial sense, there exists an immense number of specific IL–solvent combinations. An exhaustive experimental or even simulation-based investigation of the chemical space spanned by such combinations can be extremely time-consuming, expensive, and nearly impossible. An alternative approach is to employ machine learning-based models developed from available databases. Although there exists prior literature that integrates machine learning to investigate mixtures of specific solvents with ILs, these models lack generalization necessitating development of a large number of ML models to handle various solvents. To remedy this shortcoming, as a part of designing green electrolytes with high ionic conductivity that can have potential applications in next-generation batteries and solar cells, this work aims to develop a unified machine learning model to predict ionic conductivity of any IL–solvent mixture system. In this regard, three models, namely, Random Forest, extreme gradient boosting (XGBoost), and artificial neural network (ANN) were formulated using the NIST ILThermo database. The dataset contained 549 unique ionic liquids from 16 cation families and 81 unique solvents, representing a total of 23 712 datapoints. SHAPLEY additive explanation (SHAP) method was used to assess the impact of various features on model prediction and their significance was compared with literature to gain physical insight about the model behavior. Finally, using the developed models, approximately 2.5 million IL–solvent mixtures at five different compositions were screened at room temperature. The high-throughput screening yielded nearly 19 000 IL–solvent mixtures for which ionic conductivity was found to exceed the ionic conductivity of conventional Li-ion battery electrolyte.

25 ENERGY STORAGE↗
NASA NTRS2015

Improving Climate Projections Using "Intelligent" Ensembles

Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.

Baker, Noel C.↗
NASA NTRS2015

Improving Climate Projections Using "Intelligent" Ensembles

Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.

Baker, Noel C.↗
NASA NTRS2021

Architectural Modeling and Analysis for Safety Engineering

Model-based development tools are increasingly being used for system-level development of safety-critical systems. Architectural and behavioral models provide important information that can be leveraged to improve the system safety analysis process. Model-based design artifacts produced in early stage development activities can be used to perform system safety analysis, reducing costs and providing accurate results throughout the system life-cycle. In this report we describe an extension to the Architecture Analysis and Design Language (AADL) that supports modeling of system behavior under failure conditions. This Safety Annex enables the independent modeling of component failures and allows safety engineers to weave various types of fault behavior into the nominal system model. The accompanying tool support uses model checking to propagate errors from their source to their effect on safety properties without the need to add separate propagation specifications. The tool also captures all minimal set of fault combinations that can cause violation of the safety properties, that can be compared to qualitative and quantitative objectives as part of the safety assessment process. We describe the Safety Annex, illustrate its use with a representative example, and discuss and demonstrate the tool support enabling an analyst to investigate the system behavior under failure conditions.

FTA↗
DOE OSTI2021

Improvement of the $\mathrm{BISON U_3Si_2}$ modeling capabilities based on multiscale developments to modeling fission gas behavior

Uranium silicide (U 3 Si 2 ) is a concept explored as a potential alternative to UO 2 fuel used in light water reactors (LWRs) since it may improve accident tolerance and economics due to its higher thermal conductivity and increased uranium density. U 3 Si 2 has been previously used in research reactors in the form of dispersion fuel, but operated at lower temperatures than commercial LWRs. The research reactor data illustrated that significant gaseous swelling occurs as the fuel burnup increases. Therefore, it is imperative to understand the fission gas behavior of U 3 Si 2 under higher temperature LWR operating conditions. In this work, molecular dynamics and phase-field modeling techniques are used to reduce the uncertainty in select modeling assumptions made in developing the fission gas behavior model for U 3 Si 2 in the BISON fuel performance code. These lower length scale informed models are then utilized in the validation of BISON U 3 Si 2 modeling capabilities to simulate the ATF-1 experiments irradiated in the Advanced Test Reactor (ATR). Sensitivity analysis (SA) and uncertainty quantification (UQ) are included as part of the validation process to identify where further experiments and lower length scale modeling would be beneficial. Here, the multiscale modeling approach utilized in this work can be applied to new fuel concepts being explored for both LWRs and advanced reactors (e.g., uranium nitride, uranium carbide).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1998

An Operational Wake Vortex Sensor Using Pulsed Coherent Lidar

NASA and FAA initiated a program in 1994 to develop methods of setting spacings for landing aircraft by incorporating information on the real-time behavior of aircraft wake vortices. The current wake separation standards were developed in the 1970's when there was relatively light airport traffic and a logical break point by which to categorize aircraft. Today's continuum of aircraft sizes and increased airport packing densities have created a need for re-evaluation of wake separation standards. The goals of this effort are to ensure that separation standards are adequate for safety and to reduce aircraft spacing for higher airport capacity. Of particular interest are the different requirements for landing under visual flight conditions and instrument flight conditions. Over the years, greater spacings have been established for instrument flight than are allowed for visual flight conditions. Preliminary studies indicate that the airline industry would save considerable money and incur fewer passenger delays if a dynamic spacing system could reduce separations at major hubs during inclement weather to the levels routinely achieved under visual flight conditions. The sensor described herein may become part of this dynamic spacing system known as the "Aircraft VOrtex Spacing System" (AVOSS) that will interface with a future air traffic control system. AVOSS will use vortex behavioral models and short-term weather prediction models in order to predict vortex behavior sufficiently into the future to allow dynamic separation standards to be generated. The wake vortex sensor will periodically provide data to validate AVOSS predictions. Feasibility of measuring wake vortices using a lidar was first demonstrated using a continuous wave (CW) system from NASA Marshall Space Flight Sensor and tested at the Volpe National Transportation Systems Center's wake vortex test site at JFK International Airport. Other applications of CW lidar for wake vortex measurement have been made more recently, including a system developed by the MIT Lincoln Laboratory. This lidar has been used for detailed measurements of wake vortex velocities in support of wake vortex model validation. The first measurements of wake vortices using a pulsed, lidar were made by Coherent Technologies, Inc. (CTI) using a 2 micron solid-state, flashlamp-pumped system operating at 5 Hz. This system was first deployed at Denver's Stapleton Airport. Pulsed lidar has been selected as the baseline technology for an operational sensor due to its longer range capability.

Barker, Ben C., Jr.↗
DOE OSTI2021

Summary of US DOE R&D Activities on Graphite Oxidation (2006–2021)

The objective of the international collaboration between United States Department of Energy (U.S.-DOE) and Generation IV International Forum (GIF) is the development of the next generation of nuclear energy systems. The current GIF Project Arrangement (PA) on Materials (2018-2022) was revised in 2019 and extended for another 10 years (2020-2030). The Work Package 1 (“Graphite”) of the extended Project Plan (PP) on Materials specifies technical tasks and High Level Deliverables for research and development (R&D) activities related to using graphite in fuel elements, reflectors, and support structures of Very High Temperature Reactors (VHTR). The graphite tasks include specification and acquisition, qualification and development of new grades, characterization of properties, and development of behavior models. Specifically, Task 1.4 (“Graphite Oxidation Behavior”) outlines planned activities related to acute oxidation by air and chronic oxidation by impurities in the helium coolant. A final report on experimental data regarding graphite oxidation behavior is scheduled for 2022 (deliverable 3.1.1.4.a). In preparation of this deliverable, this document summarizes the R&D activities funded by U.S.-DOE from 2006 (the inception of the VHTR system arrangement) through present (2021). This report is being submitted to the GIF Graphite Working Group (GWG) to serve as input for the GWG high-level deliverable to the Project Management Board (PMB) of PA on Materials. Besides U.S.-DOE, other organizations participating to Task 1.4 of the current PA on Materials are: European Commission’s Joint Research Center (JRC), Korea Atomic Energy Research Institute (KAERI), and Japan Atomic Energy Agency (JAEA). U.S.-DOE is the main contributor on graphite oxidation R&D, with 85 % commitment of total funding during 2018-2022.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1983

Conceptual models of information processing

The conceptual information processing issues are examined. Human information processing is defined as an active cognitive process that is analogous to a system. It is the flow and transformation of information within a human. The human is viewed as an active information seeker who is constantly receiving, processing, and acting upon the surrounding environmental stimuli. Human information processing models are conceptual representations of cognitive behaviors. Models of information processing are useful in representing the different theoretical positions and in attempting to define the limits and capabilities of human memory. It is concluded that an understanding of conceptual human information processing models and their applications to systems design leads to a better human factors approach.

Stewart, L. J.↗
NASA NTRS2010

AutoGen Version 5.0

Version 5.0 of the AutoGen software has been released. Previous versions, variously denoted Autogen and autogen, were reported in two articles: Automated Sequence Generation Process and Software (NPO-30746), Software Tech Briefs (Special Supplement to NASA Tech Briefs), September 2007, page 30, and Autogen Version 2.0 (NPO- 41501), NASA Tech Briefs, Vol. 31, No. 10 (October 2007), page 58. To recapitulate: AutoGen (now signifying automatic sequence generation ) automates the generation of sequences of commands in a standard format for uplink to spacecraft. AutoGen requires fewer workers than are needed for older manual sequence-generation processes, and greatly reduces sequence-generation times. The sequences are embodied in spacecraft activity sequence files (SASFs). AutoGen automates generation of SASFs by use of another previously reported program called APGEN. AutoGen encodes knowledge of different mission phases and of how the resultant commands must differ among the phases. AutoGen also provides means for customizing sequences through use of configuration files. The approach followed in developing AutoGen has involved encoding the behaviors of a system into a model and encoding algorithms for context-sensitive customizations of the modeled behaviors. This version of AutoGen addressed the MRO (Mars Reconnaissance Orbiter) primary science phase (PSP) mission phase. On previous Mars missions this phase has more commonly been referred to as mapping phase. This version addressed the unique aspects of sequencing orbital operations and specifically the mission specific adaptation of orbital operations for MRO. This version also includes capabilities for MRO s role in Mars relay support for UHF relay communications with the MER rovers and the Phoenix lander.

Gladden, Roy E.↗
NASA NTRS1981

Benefits of detailed models of muscle activation and mechanics

Recent biophysical and physiological studies identified some of the detailed mechanisms involved in excitation-contraction coupling, muscle contraction, and deactivation. Mathematical models incorporating these mechanisms allow independent estimates of key parameters, direct interplay between basic muscle research and the study of motor control, and realistic model behaviors, some of which are not accessible to previous, simpler, models. The existence of previously unmodeled behaviors has important implications for strategies of motor control and identification of neural signals. New developments in the analysis of differential equations make the more detailed models feasible for simulation in realistic experimental situations.

Lehman, S. L.↗
DOE OSTI2021

Material Interactions in Severe Accidents – Benchmarking the MELCOR V2.2 Eutectics Model for a BWR-3 Mark-I Station Blackout: Part II – Uncertainty Analysis

Single case comparisons between severe accident simulations can provide detailed insights into severe accident model behavior, however, they cannot offer insights into model uncertainty, sensitivity to uncertain parameters, or underlying model biases.Here in this analysis, the single case benchmark comparison of the MELCOR material interaction models for a station blackout (SBO) scenario of a boiling water reactor (BWR) using representative Fukushima Daiichi Unit 1 boundary conditions is expanded to include an uncertainty analysis. As part of this uncertainty analysis, 1200 simulations are performed for each material interaction model (2400 total), with random sampling of 14 uncertain MELCOR input parameters. Input parameters are selected for their impact on models representing core degradation processes. These include candling, fuel rod failure, debris quenching and dryout. The analysis performed here is not a traditional “best-estimate” uncertainty analysis that uses best-estimate parameters or identifies best-estimate figure of merit distributions. Instead, it is an exploratory uncertainty analysis that identifies and interrogates underlying model form biases of the two material interaction models (eutectics and interactive materials models). Uniform distributions are applied to all uncertain parameters to ensure coverage of the model parameter uncertainty space. Key findings from this study include underlying model form biases exhibited by material interaction models, and notable differences in accident progression outcomes between the material interaction models. This uncertainty study extends and confirms the conclusions from the first part of this study, which compared the impact of material interaction modeling on simulation of a short-term station blackout scenario with representative Fukushima Daiichi Unit I boundary conditions. In particular, this study confirms that the eutectics model generally exhibits accelerated degradation and failure of fuel components, the core plate, and the lower head. The eutectics model also has a tendency to exhibit a greater degree of core degradation, greater debris mass formation, and larger debris mass ejection. Finally, the eutectics model exhibits higher maximum temperatures for fuel, cladding, particulate debris, oxidic molten pool, and metallic molten pool components than the interactive materials model; interactive materials model simulations exhibit a soft “limitation” on maximum temperatures that is related to the temperature at which material relocation occurs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1991

Design Strategy for a Formally Verified Reliable Computing Platform

This paper presents a high-level design for a reliable computing platform for real-time control applications. The design tradeoffs and analyses related to the development of a formally verified reliable computing platform are discussed. The design strategy advocated in this paper requires the use of techniques that can be completely characterized mathematically as opposed to more powerful or more flexible algorithms whose performance properties can only be analyzed by simulation and testing. The need for accurate reliability models that can be related to the behavior models is also stressed. Tradeoffs between reliability and voting complexity are explored. In particular, the transient recovery properties of the system are found to be fundamental to both the reliability analysis as well as the "correctness" models.

Butler, Ricky W.↗
DOE OSTI2022

Development of a water source heat pump hardware-in-the-loop (HIL) testing facility for smart building applications

Over the last decade, the global fight against climate change through electrification has led to an increase in research on building heating, ventilation, and air conditioning (HVAC) systems that utilize intelligent control algorithms to provide demand-side grid service while maintaining the thermal comfort of building occupants. As the pivotalpoint between building electricity consumption and indoor thermal comfort, high-efficiency electrical heatpumps are at the center of these emerging studies, and various grid-interactive and occupant-comfort control algorithms have been developed for them. The impact of these algorithms on the heatpump operation andperformance under different weather, building load, and grid requests calls for investigation and verification via experimental tests with actual heat pumps integrated with real-time building and grid responses. This study presents a Water-Source Heat Pump Hardware-in-The-Loop (HIL) Test Facility developed with the capability to perform such tests. The hardware configuration for this testfacility introduces a hydronic system that emulates the conditions for the heat pump water-side, and a duct system that emulates conditions for the heat pump airside. Both data acquisition and emulator control are implemented through the National Instruments (NI) LabVIEW software running on an NI PXIplatform. The HIL mechanism based on the hardware-software integration that allows the testbed to communicate with a generic simulation environment is also discussed. Currently, the test facility setup includes a single heatpump and virtual building model in EnergyPlus coupled with an occupant behavioral model in MATLAB. Preliminary test results of the current setup demonstrate the building load emulator's ability to track the simulated gone temperature with a Root Mean Square Deviation (RSME) below 0.12°C (0.216°F). An uncertainty analysis based on sensor accuracies shows that the heat pump coefficient of performance (COP) can be measured with a relative uncertainty of 10.4% in cooling and 3.7% in heating. Apartfrom the current testing on a single heat pump, the test facility also provides the flexibility to include additional heat pumps to form a heat pump cluster, as well as coupling the heat pump with active thermal storage to provide enhanced demandflexibility.

Calfa, Caleb↗