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Modeling and Analysis of Stirling Power Convertors

Modeling and Analysis of Stirling Power Convertors Luis A. Rodriguez1 Steven M. Geng, Terry V. Reid, Scott D. Wilson NASA Glenn Research Center, Cleveland, OH, 44135, USA NASA Glenn’s Thermal Energy Conversion Branch is supporting the development of the next generation free-piston Stirling power convertors. American Superconductor (AMSC) and Sunpower Inc. are the two firms under contract to develop the Flexure Isotope Stirling Convertor (FISC) and the Sunpower Robust Stirling Convertor (SRSC), respectively. To comprehend and forecast convertor performance, Sage, ANSYS® Maxwell, and ANSYS® Fluent were used to model the Stirling thermodynamic cycle, alternator electromagnetics, and piston and displacer dynamics. I. Introduction Stirling convertors are being developed by NASA as a potential steady source of electrical power for NASA’s future scientific space missions. Currently, NASA Glenn Research Center has two corporations under contract, American Superconductor (AMSC) and Sunpower Inc., for the development of the next generation of free-piston Stirling convertors for dynamic radioisotope power systems. AMSC is developing the Flexure Isotope Stirling Convertor (FISC), which uses flexures to prevent side motion and rubbing of the piston. Similarly, Sunpower Inc, is developing the Sunpower Robust Stirling Convertor (SRSC). The SRSC uses gas bearings to prevent radial contact of the moving piston. As convertor development continues, it is increasingly important to understand and predict the interactions of components in the system, how they respond to one another, and how they perform as a response to changes in operating conditions. A suitable and enlightening way to demonstrate and foresee these interactions is with the use of accurate modeling software. Sage, ANSYS® Maxwell, and ANSYS® Fluent are the current modeling tools used by NASA to analytically determine convertor performance. Sage is a one-dimensional object-oriented commercial software package used for modeling and optimizing Stirling convertors for Dynamic Radioisotope Power Systems (DRPS) and it is one of the most accurate Stirling convertor codes in use by NASA. This code is the successor to GLIMPS (Globally-Implicit Stirling Cycle Simulation) and GLOP (GLIMPS Optimization) software created by Gedeon Associates [1]. Model input parameters are typically material/gas type, component physical dimensions, temperatures, frequency, charge pressure, and number of time/space nodes. Sage is used to model both the FISC’s and SRSC’s Stirling cycle thermodynamics and piston/displacer dynamics. Performance maps were created and analyzed for both power systems to better understand the relationship between the following conditions: cold-end temperature, hot-end temperature, piston/displacer amplitudes, pressure drop, and thermal input power. The synergy between these conditions will help determine parameter sensitivity. ANSYS® Maxwell was used to create a three-dimensional (3-D) axisymmetric model for both FISC and SRSC alternators. The significant physical components included in each model are the magnets, magnet carrier, outer/inner laminations, and the coil. Inputs to the model are piston amplitude, piston frequency, alternator load, coil resistance, tuning capacitance, and specific material properties. The alternator models calculate terminal voltage, current, piston/current phase, voltage/current phase, coil inductance, terminal power and efficiency. The RI2 losses, core (hysteresis and eddy) losses, and magnet/can eddy losses are also a part of the final results. ANSYS® Fluent is used to build 3-D computational fluid dynamic (CFD) models to examine the Stirling cycle thermodynamics for both the FISC and SRSC systems. Three-dimensional Computer Aided Design (CAD) models were used to create the physical components of each convertor. Steady-state simulations were conducted for hardware testing, prediction of environmental losses during testing, and generation of radiation look-up tables. The model inputs to the aforementioned analysis are the material properties and boundary thermal conditions. The steady-state model calculates temperature and heat flow distributions. Transient 3-D calculations were also part of the CFD analysis. In this study a physically reduced version of the FISC is used to obtain a prediction of available engine power. For the gas bearing SRSC, the transient effort is used to obtain a prediction of bearing pad performance and its sensitivity to micro-channel geometric variation. The model inputs to the transient simulations are the piston amplitude, displacer amplitude, frequency, displacer/piston phase angle, dynamic deforming CFD grid, temperature boundary conditions, and user defined files describing motion profile of piston/displacer. The results of the model are temperature distributions, heat distributions, and PV power produced at pre-determined conditions.

Luis A Rodriguez↗

DOE OSTI2014

SU-E-J-148: Evaluating Tumor Response with a Commercially Available Deformable Registration System

Purpose: The purpose of this study is to present a method for evaluating the response to treatment using a commercially available deformable image registration software package (Velocity Medical Systems) and repeat PET/CT imaging. This technique can be used to identify volumes that are risk for tumor recurrence. Methods: Response to treatment was evaluated using PET/CT images acquired prior-to and post-treatment for radiation therapy patients treated with concurrent chemotherapy. Velocity (Version 3.0.1) was used to deform the initial PET/CT to the post treatment PET/CT. The post-treatment PET images were then subtracted from the pre-treatment PET images. The resulting re-sampled image is a three-dimensional SUV difference map that shows pixels with increasing SUV values. SUV values increases greater than 2.5 in the post treatment images were identified for additional follow-up. Results: A total of 5 Lung patients were analyzed as part of this study. One lung patient in the cohort had an SUV increase of +3.28 that was identified using the SUV difference map. This volume of increased uptake was located outside the treatment field and adjacent to the 35 Gy isodose line. The remaining four patients all had SUV decreases inside the planning target volume, and no unexpected areas of increase outside the irradiated volumes. All five patients were analyzed using standard tools inside the Velocity application. Conclusion: The response to treatment can easily be measured using serial PET/CT images and a commercially available deformable image registration. This provides both the radiation oncologists and medical oncologists with a quantitative assessment of their treatment to use in patient follow-up.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2016

Space Suit Portable Life Support System (PLSS) 2.0 Pre-Installation Acceptance (PIA) Testing

Following successful completion of the space suit Portable Life Support System (PLSS) 1.0 development and testing in 2011, the second system-level prototype, PLSS 2.0, was developed in 2012 to continue the maturation of the advanced PLSS design which is intended to reduce consumables, improve reliability and robustness, and incorporate additional sensing and functional capabilities over the current Space Shuttle/International Space Station Extravehicular Mobility Unit (EMU) PLSS. PLSS 2.0 represents the first attempt at a packaged design comprising first generation or later component prototypes and medium fidelity interfaces within a flight-like representative volume. Pre-Installation Acceptance (PIA) is carryover terminology from the Space Shuttle Program referring to the series of test sequences used to verify functionality of the EMU PLSS prior to installation into the Space Shuttle airlock for launch. As applied to the PLSS 2.0 development and testing effort, PIA testing designated the series of 27 independent test sequences devised to verify component and subsystem functionality, perform in situ instrument calibrations, generate mapping data to define set-points for control algorithms, evaluate hardware performance against advanced PLSS design requirements, and provide quantitative and qualitative feedback on evolving design requirements and performance specifications. PLSS 2.0 PIA testing was carried out from 3/20/13 - 3/15/14 using a variety of test configurations to perform test sequences that ranged from stand-alone component testing to system-level testing, with evaluations becoming increasingly integrated as the test series progressed. Each of the 27 test sequences was vetted independently, with verification of basic functionality required before completion. Because PLSS 2.0 design requirements were evolving concurrently with PLSS 2.0 PIA testing, the requirements were used as guidelines to assess performance during the tests; after the completion of PIA testing, test data served to improve the fidelity and maturity of design requirements as well as plans for future advanced PLSS functional testing.

Watts, Carly↗

NASA NTRS2016

Space Suit Portable Life Support System (PLSS) 2.0 Pre-Installation Acceptance (PIA) Testing

Following successful completion of the space suit Portable Life Support System (PLSS) 1.0 development and testing in 2011, the second system-level prototype, PLSS 2.0, was developed in 2012 to continue the maturation of the advanced PLSS design. This advanced PLSS is intended to reduce consumables, improve reliability and robustness, and incorporate additional sensing and functional capabilities over the current Space Shuttle/International Space Station Extravehicular Mobility Unit (EMU) PLSS. PLSS 2.0 represents the first attempt at a packaged design comprising first generation or later component prototypes and medium fidelity interfaces within a flight-like representative volume. Pre-Installation Acceptance (PIA) is carryover terminology from the Space Shuttle Program referring to the series of test sequences used to verify functionality of the EMU PLSS prior to installation into the Space Shuttle airlock for launch. As applied to the PLSS 2.0 development and testing effort, PIA testing designated the series of 27 independent test sequences devised to verify component and subsystem functionality, perform in situ instrument calibrations, generate mapping data, define set-points, evaluate control algorithms, evaluate hardware performance against advanced PLSS design requirements, and provide quantitative and qualitative feedback on evolving design requirements and performance specifications. PLSS 2.0 PIA testing was carried out in 2013 and 2014 using a variety of test configurations to perform test sequences that ranged from stand-alone component testing to system-level testing, with evaluations becoming increasingly integrated as the test series progressed. Each of the 27 test sequences was vetted independently, with verification of basic functionality required before completion. Because PLSS 2.0 design requirements were evolving concurrently with PLSS 2.0 PIA testing, the requirements were used as guidelines to assess performance during the tests; after the completion of PIA testing, test data served to improve the fidelity and maturity of design requirements as well as plans for future advanced PLSS functional testing.

Anchondo, Ian↗

DOE OSTI2014

sparse-msrf:A package for sparse modeling and estimation of fossil-fuel CO2 emission fields

The software is used to fit models of emission fields (e.g., fossil-fuel CO2 emissions) to sparse measurements of gaseous concentrations. Its primary aim is to provide an implementation and a demonstration for the algorithms and models developed in J. Ray, V. Yadav, A. M. Michalak, B. van Bloemen Waanders and S. A. McKenna, "A multiresolution spatial parameterization for the estimation of fossil-fuel carbon dioxide emissions via atmospheric inversions", accepted, Geoscientific Model Development, 2014. The software can be used to estimate emissions of non-reactive gases such as fossil-fuel CO2, methane etc. The software uses a proxy of the emission field being estimated (e.g., for fossil-fuel CO2, a population density map is a good proxy) to construct a wavelet model for the emission field. It then uses a shrinkage regression algorithm called Stagewise Orthogonal Matching Pursuit (StOMP) to fit the wavelet model to concentration measurements, using an atmospheric transport model to relate emission and concentration fields. Algorithmic novelties described in the paper above (1) ensure that the estimated emission fields are non-negative, (2) allow the use of guesses for emission fields to accelerate the estimation processes and (3) ensure that under/overestimates in the guesses do not skew the estimation.

None, JinaLee↗

DOE OSTI2014

sparse-msrf:A package for sparse modeling and estimation of fossil-fuel CO2 emission fields

Source record↗

DOE OSTI2016

Development of the methods for simulating the neutron spectrometers and neutron-scattering experiments

Reviewed are the results of simulating the neutron scattering instruments with the program package VITESS upgraded by the routines for treating the polarized neutrons, as developed by the authors. The reported investigations have been carried out at the Frank Laboratory for Neutron Physics at JINR in collaboration with the Juelich research center (Germany). The performance of the resonance and gradient adiabatic spin flippers, the Drabkin resonator, the classical and resonance spin-echo spectrometers, the spin-echo diffractometer for the small-angle neutron scattering, and the spin-echo spectrometer with rotating magnetic fields is successfully modeled. The methods for using the 3D map of the magnetic field from the input file, either mapped experimentally or computed using the finite-elements technique, in the VITESS computer code, are considered in detail. The results of neutron-polarimetry experiments are adequately reproduced by our simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

WHONDRS 2016 Sediment Organic Matter Characterization Data from Streams across HJ Andrews Experimental Forest, Oregon

This dataset supports a broader synoptic effort to map morphological, hydrological, chemical, and biological conditions across a fifth-order mountain stream network. Samples were generated through a collaborative synoptic sampling effort in 2016. The dataset provides sediment Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS) from 60 sites across the HJ Andrews Experimental Forest, Oregon (https://andrewsforest.oregonstate.edu). Related data were collected as part of the event and were published separately in collaboration with other team members. The data are available at http://www.hydroshare.org/resource/ea6c0832885a46c3939e7bb22e48e754 and are described within https://doi.org/10.5194/essd-11-1567-2019 (Ward et al., 2019). The hydroshare data package contains processed FTICR-MS data from the samples included in this data package. The data were processed via Formultitude (previously called Formularity; https://github.com/PNNL-Comp-Mass-Spec/Formultitude). However, we have re-processed the data using Core-MS and included it in this data package. Additional related data collected in 2025 from a similar effort can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3023310 and http://www.hydroshare.org/resource/b274c4a234bf4b12b7cb8a54a696c629. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) a folder of sample data; (2) data dictionary; (3) file-level metadata; (4); (5) coordinates; and (6) readme. The sample data subfolder contains 12 Tesla (12T) FTICR-MS data. This folder contains the processed data and three subfolders, one containing the .xml files, one containing the CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .Rmd, .py, .cal, or .json.

Biogeochemistry↗

DOE OSTI2018

Schedule Management Optimization (SMO) Domain Model (V.1.2)

Schedule Management Optimization (SMO) is a tool for automatically generating a schedule of project tasks. Project scheduling is traditionally achieved with the use of commercial project management software or case-specific optimization formulations. Commercial software packages are useful tools for managing and visualizing copious amounts of project task data. However, their ability to automatically generate optimized schedules is limited. Furthermore, there are many real-world constraints and decision variables that commercial packages ignore. Case-specific optimization formulations effectively identify schedules that optimize one or more objectives for a specific problem, but they are unable to handle a diverse selection of scheduling problems. SMO enables practitioners to generate optimal project schedules automatically while considering a broad range of real-world problem characteristics. SMO has been designed to handle some of the most difficult scheduling problems – those with resource constraints, multiple objectives, multiple inventories, and diverse ways of performing tasks. This report contains descriptions of the SMO modeling concepts and explains how they map to real-world scheduling considerations.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2000

Salinity Measurements During the Gulf Stream Experiment

The salinity of the open ocean is important for understanding ocean circulation, for understanding energy exchange with the atmosphere and for improving models to predict weather and climate. Passive microwave sensors at L-band (1.4 GHz) operating from aircraft have demonstrated that salinity can be measured with sufficient accuracy (1 psu) to be scientifically meaningful in coastal waters. However, measuring salinity in the open ocean presents unresolved issues largely because of the much greater accuracy (approx. 0.1 psu) required to be scientifically viable. In the summer of 1999 a series of measurements called, The Gulf Stream Experiment, were conducted as part of research at the Goddard Space Flight Center to test the potential for passive microwave remote sensing of salinity in the open ocean. The measurements consisted of a compliment of airborne microwave instruments (radiometers and scatterometer) and ships and drifters for surface truth. The study area was a 200 km by 100 km rectangle about 250 km east of Delaware Bay between the continental shelf waters and north wall of the Gulf Stream. The primary passive instruments were the ESTAR radiometer (L-band, H-pol) and the SLFMR radiometer (L-band, V-pol). In addition, the compliment of instruments on the aircraft included a C-band radiometer (ACMR), an ocean wave scatterometer (ROWS) and an infrared radiometer. A GPS backscatter experiment was also part of the package. These instruments were mounted on the NASA P-3 Orion aircraft. Surface salinity measurements were provided by the RN Cape Henlopen and MN Oleander (thermosalinographs) plus salinity and temperature sensors on three surface drifters deployed from the RN Cape Henopen. The primary experiment period was August 26-September 2, 1999. During this period the salinity field within the study area consisted of a gradient on the order of 2-3 psu in the vicinity of the shelf break and a warm core ring with a gradient of 1-2 psu. Detailed maps were made with the airborne sensors on August 28 and 29 and on September 2 flights were made over the surface drifters to look for effects due to a change in surface roughness resulting from the passage of Hurricane Dennis. Preliminary results show a good agreement between the microwave measurements and ship measurements of salinity. The features of the brightness temperature maps correspond well with the features of the salinity field measured by the ship and drifters and a preliminary retrieval of salinity compares well with the ship data.

LeVine, D. M.↗

DOE OSTI2015

AN ATLAS OF CORONAL ELECTRON DENSITY AT 5R{sub ⊙}. I. DATA PROCESSING AND CALIBRATION

Tomography of the solar corona can provide cruicial constraints for models of the low corona, unique information on changes in coronal structure and rotation rates, and a valuable boundary condition for models of the heliospheric solar wind. This is the first of a series of three papers which aim to create a set of maps of the coronal density over an extended period (1996-present). The papers will describe the data processing and calibration (this paper), the tomography method (Paper II), and the resulting atlas of coronal electron density at a height of 5 R{sub ⊙} between years 1996–2014 (Paper III). This first paper presents a detailed description of data processing and calibration for the Large-Angle and Spectrometric Coronagraph (LASCO) C2 instrument on board the Solar and Heliospheric Observatory (SOHO) and the COR2 instruments of the Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) package on board the Solar Terrestial Relations Observatory (STEREO) A and B spacecraft. The methodology includes noise suppression, background subtraction, separation of large dynamic events, conversion of total brightness to K-coronal brightness, and simple functions for cross-calibration between C2/LASCO and COR2/SECCHI. Comparison of the brightness of stars between LASCO C2 total and polarized brightness (pB) observations provide in-flight calibration factors for the pB observations, resulting in considerable improved agreement between C2 and COR2 A, and elimination of curious artifacts in the C2 pB images. The cross-calibration between LASCO C2 and the STEREO coronagraphs allows, for the first time, the potential use of multi-spacecraft coronagraph data for tomography and for coronal mass ejection analysis.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1994

FORTRAN programs to process Magsat data for lithospheric, external field, and residual core components

The FORTRAN programs supplied in this document provide a complete processing package for statistically extracting residual core, external field and lithospheric components in Magsat observations. To process the individual passes: (1) orbits are separated into dawn and dusk local times and by altitude, (2) passes are selected based on the variance of the magnetic field observations after a least-squares fit of the core field is removed from each pass over the study area, and (3) spatially adjacent passes are processed with a Fourier correlation coefficient filter to separate coherent and non-coherent features between neighboring tracks. In the second state of map processing: (1) data from the passes are normalized to a common altitude and gridded into dawn and dusk maps with least squares collocation, (2) dawn and dusk maps are correlated with a Fourier correlation efficient filter to separate coherent and non-coherent features; the coherent features are averaged to produce a total field grid, (3) total field grids from all altitudes are continued to a common altitude, correlation filtered for coherent anomaly features, and subsequently averaged to produce the final total field grid for the study region, and (4) the total field map is differentially reduced to the pole.

Alsdorf, Douglas E.↗

NASA NTRS2015

RESOLVE - Regolith and Environment Science and Oxygen and Lunar Volatile Extraction

The Regolith & Environment Science and Oxygen & Lunar Volatile Extraction (RESOLVE) payload is an exploration system designed to be placed on a rover and driven over the surface of the moon for 9 days to map the distribution of the water ice and other useful compounds seen on previous missions. RESOLVE will drill into the lunar surface and heat the material collected in order to measure the amount of water vapor and other compounds that are present, thus showing how future missions could gather and then use these valuable resources. Future missions will benefit from this analysis tool and others because it will be more cost-effective to mine water components, fuel, and other compounds at the point of destination rather than transport them from Earth. NASA is packaging the RESOLVE payload in the Resource Prospector mission targeted for launch in 2020. NASA continues to explore mission solutions by leveraging partnerships across NASA, industry, other nations and academia.

RESOLVE↗

DOE OSTI2014

Processing of X-ray snapshots from crystals in random orientations

A new method for the treatment of partial reflections from X-ray snapshots is implemented in the program package nXDS, which yields intensity data of almost the same quality as those obtained by the classical rotation method. A functional expression is introduced that relates scattered X-ray intensities from a still or a rotation snapshot to the corresponding structure-factor amplitudes. The new approach was implemented in the program nXDS for processing monochromatic diffraction images recorded by a multi-segment detector where each exposure could come from a different crystal. For images containing indexable spots, the intensities of the expected reflections and their variances are obtained by profile fitting after mapping the contributing pixel contents to the Ewald sphere. The varying intensity decline owing to the angular distance of the reflection from the surface of the Ewald sphere is estimated using a Gaussian rocking curve. This decline is dubbed ‘Ewald offset correction’, which is well defined even for still images. Together with an image-scaling factor and other corrections, an explicit expression is defined that predicts each recorded intensity from its corresponding structure-factor amplitude. All diffraction parameters, scaling and correction factors are improved by post-refinement. The ambiguous case of a lower point group than the lattice symmetry is resolved by a method reminiscent of the technique of ‘selective breeding’. It selects the indexing alternative for each image that yields, on average, the highest correlation with intensities from all other images. Processing a test set of rotation images by XDS and treating the same images by nXDS as snapshots of crystals in random orientations yields data of comparable quality, clearly indicating an anomalous signal from Se atoms.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS2003

Predicting and Managing Lighting and Visibility for Human Operations in Space

Lighting is critical to human visual performance. On earth this problem is well understood and solutions are well defined and executed. Because the sun rises and sets on average every 45 minutes during Earth orbit, humans working in space must cope with ~ extremely dynamic lighting conditions varying from very low light conditions to severe glare and contrast conditions. For critical operations, it is essential that lighting conditions be predictable and manageable. Mission planners need to detelmine whether low-light video cameras are required or whether additional luminaires, or lamps, need to be flown . Crew and flight directors need to have up to date daylight orbit time lines showing the best and worst viewing conditions for sunlight and shadowing. Where applicable and possible, lighting conditions need to be part of crew training. In addition, it is desirable to optimize the quantity and quality of light because of the potential impacts on crew safety, delivery costs, electrical power and equipment maintainability for both exterior and interior conditions. Addressing these issues, an illumination modeling system has been developed in the Space Human Factors Laboratory at ASA Johnson Space Center. The system is the integration of a physically based ray-tracing package ("Radiance"), developed at the Lawrence Berkeley Laboratories, a human factors oriented geometric modeling system developed by NASA and an extensive database of humans and their work environments. Measured and published data has been collected for exterior and interior surface reflectivity; luminaire beam spread distribution, color and intensity and video camera light sensitivity and has been associated with their corresponding geometric models. Selecting an eye-point and one or more light sources, including sun and earthshine, a ~ snapshot of the light energy reaching the surfaces or reaching the eye point is computed. This energy map is then used to extract the required information needed for useful predictions. Using a validated, comprehensive illumination model integrated with empirically derived data, predictions of lighting and viewing conditions have been successfully used for Shuttle and Space Station planning and assembly operations. It has successfully balanced the needs for adequate human performance with the utili zation of resources. Keywords: Modeling, ray tracing, luminaires, refl ectivity, luminance, illuminance.

Maida, James C.↗

DOE OSTI2020

Architecture and self-assembly of the SARS-CoV-2 nucleocapsid protein

Abstract not provided

Ye, Qiaozhen↗

DOE OSTI2016

Geospatial Toolkit

The Geospatial Toolkit is an NREL-developed map-based software application that integrates resource data and other geographic information systems (GIS) data for integrated resource assessment. The non-resource, country-specific data for each toolkit comes from a variety of agencies within each country as well as from global datasets. Originally developed in 2005, the Geospatial Toolkit was completely redesigned and re-released in November 2010 to provide a more modern, easier-to-use interface with considerably faster analytical querying capabilities. The revised version of the Geospatial Toolkit has been released for all original toolkit countries/regions and each software package is made available on NREL's website,

Source record↗

DOE OSTI2017

A Systematic Classification for HVAC Systems and Components

Depending on the application, the complexity of an HVAC system can range from a small fan coil unit to a large centralized air conditioning system with primary and secondary distribution loops, and central plant components. Currently, the taxonomy of HVAC systems and the components has various aspects, which can get quite complex because of the various components and system configurations. For example, based on cooling and heating medium delivered to terminal units, systems can be classified as either air systems, water systems or air-water systems. In addition, some of the system names might be commonly used in a confusing manner, such as “unitary system” vs. “packaged system.” Without a systematic classification, these components and system terminology can be confusing to understand or differentiate from each other, and it creates ambiguity in communication, interpretation, and documentation. It is valuable to organize and classify HVAC systems and components so that they can be easily understood and used in a consistent manner. This paper aims to develop a systematic classification of HVAC systems and components. First, we summarize the HVAC component information and definitions based on published literature, such as ASHRAE handbooks, regulations, and rating standards. Then, we identify common HVAC system types and map them to the collected components in a meaningful way. Classification charts are generated and described based on the component information. Six main categories are identified for the HVAC components and equipment, i.e., heating and cooling production, heat extraction and rejection, air handling process, distribution system, terminal use, and stand-alone system. Components for each main category are further analyzed and classified in detail. More than fifty system names are identified and grouped based on their characteristics. The result from this paper will be helpful for education, communication, and systems and component documentation.

Li, Han↗