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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Physical and Dynamical Linkages Between Lightning Jumps and Storm Conceptual Models

The presence and rates of total lightning are both correlated to and physically dependent upon storm updraft strength, mixed phase precipitation volume and the size of the charging zone. The updraft modulates the ingredients necessary for electrification within a thunderstorm, while the updraft also plays a critical role in the development of severe and hazardous weather. Therefore utilizing this relationship, the monitoring of lightning rates and jumps provides an additional piece of information on the evolution of a thunderstorm, more often than not, at higher temporal resolution than current operational radar systems. This correlation is the basis for the total lightning jump algorithm that has been developed in recent years. Currently, the lightning jump algorithm is being tested in two separate but important efforts. Schultz et al. (2014; this conference) is exploring the transition of the algorithm from its research based formulation to a fully objective algorithm that includes storm tracking, Geostationary Lightning Mapper (GLM) Proxy data and the lightning jump algorithm. Chronis et al. (2014) provides context for the transition to current operational forecasting using lightning mapping array based products. However, what remains is an end-to-end physical and dynamical basis for coupling total lightning flash rates to severe storm manifestation, so the forecaster has a reason beyond simple correlation to utilize the lightning jump algorithm within their severe storm conceptual models. Therefore, the physical basis for the lightning jump algorithm in relation to severe storm dynamics and microphysics is a key component that must be further explored. Many radar studies have examined flash rates and their relationship to updraft strength, updraft volume, precipitation-sized ice mass, etc.; however, their relationship specifically to lightning jumps is fragmented within the literature. Thus the goal of this study is to use multiple Doppler and polarimetric radar techniques to resolve the physical and dynamical storm characteristics specifically around the time of the lightning jump. This information will help forecasters anticipate lightning jump occurrence, or even be of use to determine future characteristics of a given storm (e.g., development of a mesocyclone, downdraft, or hail signature on radar), providing additional lead time/confidence in the severe storm warning paradigm.

Schultz, Christopher J.↗
NASA NTRS2015

A Prototype Physical Database for Passive Microwave Retrievals of Precipitation over the US Southern Great Plains

An accurate understanding of the instantaneous, dynamic land surface emissivity is necessary for a physically based, multi-channel passive microwave precipitation retrieval scheme over land. In an effort to assess the feasibility of the physical approach for land surfaces, a semi-empirical emissivity model is applied for calculation of the surface component in a test area of the US Southern Great Plains. A physical emissivity model, using land surface model data as input, is used to calculate emissivity at the 10GHz frequency, combining contributions from the underlying soil and vegetation layers, including the dielectric and roughness effects of each medium. An empirical technique is then applied, based upon a robust set of observed channel covariances, extending the emissivity calculations to all channels. For calculation of the hydrometeor contribution, reflectivity profiles from the Tropical Rainfall Measurement Mission Precipitation Radar (TRMM PR) are utilized along with coincident brightness temperatures (Tbs) from the TRMM Microwave Imager (TMI), and cloud-resolving model profiles. Ice profiles are modified to be consistent with the higher frequency microwave Tbs. Resulting modeled top of the atmosphere Tbs show correlations to observations of 0.9, biases of 1K or less, root-mean-square errors on the order of 5K, and improved agreement over the use of climatological emissivity values. The synthesis of these models and data sets leads to the creation of a simple prototype Tb database that includes both dynamic surface and atmospheric information physically consistent with the land surface model, emissivity model, and atmospheric information.

microwave↗
NASA NTRS2016

Investigation of Damping Physics and CFD Tool Validation for Simulation of Baffled Tanks at Variable Slosh Amplitude

Determination of slosh damping is a very challenging task as there is no analytical solution. The damping physics involves the vorticity dissipation which requires the full solution of the nonlinear Navier-Stokes equations. As a result, previous investigations were mainly carried out by extensive experiments. A systematical study is needed to understand the damping physics of baffled tanks, to identify the difference between the empirical Miles equation and experimental measurements, and to develop new semi-empirical relations to better represent the real damping physics. The approach of this study is to use Computational Fluid Dynamics (CFD) technology to shed light on the damping mechanisms of a baffled tank. First, a 1-D Navier-Stokes equation representing different length scales and time scales in the baffle damping physics is developed and analyzed. Loci-STREAM-VOF, a well validated CFD solver developed at NASA MSFC, is applied to study the vorticity field around a baffle and around the fluid-gas interface to highlight the dissipation mechanisms at different slosh amplitudes. Previous measurement data is then used to validate the CFD damping results. The study found several critical parameters controlling fluid damping from a baffle: local slosh amplitude to baffle thickness (A/t), surface liquid depth to tank radius (d/R), local slosh amplitude to baffle width (A/W); and non-dimensional slosh frequency. The simulation highlights three significant damping regimes where different mechanisms dominate. The study proves that the previously found discrepancies between Miles equation and experimental measurement are not due to the measurement scatter, but rather due to different damping mechanisms at various slosh amplitudes. The limitations on the use of Miles equation are discussed based on the flow regime.

Yang, H. Q.↗
NASA NTRS2019

How Certain Physical Considerations Impact Aerostructural Wing Optimization

Wing design optimization has been studied extensively and is of continued interest as optimization tools are developed and become more accessible. In each of these studies, certain assumptions and simplifications are made to make the design problem tractable. However, it is difficult to find systematic studies in which several considerations are added or removed one at a time to study how much impact they have. In this work, we examine how certain physical considerations (viscous drag, wave drag, thrust loads, and inertial relief from structural, fuel, and engine masses), impact the aerostructural optimization results for three distinct aircraft wings. The goal is to help develop a rough idea of how important these physical considerations are. We do this using gradient-based optimization and a multidisciplinary design optimization framework, OpenMDAO. We use the open-source tool OpenAeroStruct that couples a vortex lattice method to a finite element method. We establish a baseline aerostructural design optimization problem then perform a series of optimizations, each with one physical consideration removed from the baseline case. We find that depending on the size of the aircraft and flight conditions, the importance of some of these physical considerations varies considerably whereas the importance of others do not. Specifically, the optimal designs change radically without proper viscous and wave drag considerations and smaller aircraft with more distributed propulsion are more affected by the inclusion of engine loads.

Aeropropulsion↗
NASA NTRS2020

Fluorescence Correction Vegetation Index (FCVI): A physically based reflectance index to separate physiological and non-physiological information in far-red sun-induced chlorophyll fluorescence

Sun-induced chlorophyll fluorescence (SIF) has been used to track vegetation photosynthetic activity for improving estimation of gross primary productivity (GPP) and detecting plant stress. There are both physical and physiological controls of SIF measured at the surface and retrieved from remote sensing including satellite observations. In order to accurately use SIF for monitoring of plant physiology, the effects of physically-based radiation processes related to leaf and canopy structure, notably photosynthetically active radiation (PAR) absorption and SIF scattering and re-absorption, must be characterized. In this study, we investigate both PAR absorption and SIF scattering processes and find that although it is difficult to quantify their effects individually by using just reflectance, the combined effects of the two processes can be well approximated by a reflectance index. This index, referred to as FCVI (Fluorescence Correction Vegetation Index), is defined as the difference between near-infrared (NIR) and broad-band visible (VIS, 400–700 nm) reflectance acquired under identical sun-canopy-observer geometry of the SIF measurements. The development of the index was based on the physical connection between reflectance and far-red SIF, which was revealed by using the spectral invariant theory. The utility of FCVI to correct far-red SIF for PAR absorption and scattering effects, thus improving the link to photosynthesis, was tested with data from: (i) a field experiment for a growing season; and (ii) a numerical experiment which included a number of scenarios generated by a radiative transfer model. For both the observations and simulations, the FCVI provided a promising estimate of the impact of the physically-based radiation processes on far-red SIF of moderately dense canopies (i.e., FCVI ≥ 0.18). Normalizing the TOC far-red SIF by both the incident PAR (iPAR) and the FCVI provided a good estimate of the far-red fluorescence emission efficiency of the canopies examined. This approach enhances our ability to generalize retrievals for vegetation processes as they change through natural growth phases and seasons. Taken together, far-red SIF and FCVI may enable the assessment of the light partitioning of vegetation canopies, an essential step to facilitate the use of far-red SIF for tracking physiological processes.

Peiqi Yang↗
NASA NTRS2023

Heat Transfer and Interfacial Flow Physics of Microgravity Flow Boiling in Single-Side-Heated Rectangular Channel with Subcooled Inlet Conditions – Experiments Onboard the International Space Station

This study is the culmination of a long-term collaborative effort between researchers from the Purdue University Boiling and Two-Phase Flow Laboratory (PU-BTPFL) and the NASA Glenn Research Center to investigate gravitational effects on flow boiling and flow condensation. The science and design concepts for this large-scale effort were initiated in 2011 and included several studies detailing various aspects of two-phase fluid physics in both Earth gravity and microgravity, culminating in construction of the large-scale experimental facility named “Flow Boiling and Condensation Experiment (FBCE)”. The experiment was launched to the International Space Station (ISS) in August 2021. Following the successful installation of FBCE, equipped with the Flow Boiling Module (FBM), onboard the ISS and completion of several safety checks, flow boiling experiments were performed for five months from February 2022 until July 2022. This resulted in a large flow boiling database covering broad ranges of operating parameters and heating configurations spanning several research objectives. This study investigates microgravity flow boiling of n-perfluorohexane with subcooled inlet in a single-side-heated rectangular channel of dimensions 114.6-mm heated length, 2.5-mm heated width, and 5.0-mm height. Key operating parameters investigated are mass velocity (199.90 – 3200.13 kg/m 2 s), inlet subcooling (0.10 – 45.76°C), and inlet pressure (113.30 – 164.29 kPa). Images and image sequences acquired via high-speed-video are presented to elucidate the interfacial flow physics. To analyze and explain the effects of various parameters in microgravity, heat transfer results are presented as flow boiling curves, streamwise profiles of wall temperature and heat transfer coefficient, and parametric trends of local and averaged heat transfer coefficient. Mass velocity and inlet subcooling significantly influenced most of the aforementioned aspects of flow boiling, whereas effects of inlet pressure were comparatively insignificant. Although the data and observed flow physics might be different, the parametric effects and trends in microgravity are similar to vertical upflow in Earth gravity. Some cases, especially low mass velocities, high heat fluxes, and large degrees of inlet subcooling, experienced temporally anomalous flow behaviors caused by two-phase flow instabilities manifesting as flow reversals and resulted in deviations in overall trends. Severe thermodynamic non-equilibrium is observed throughout the channel. Overall, FBCE’s ISS experiments were successful for subcooled inlet with single-sided heating of rectangular channel, and the collected data well established the various effects on flow boiling physics in highly controlled long-term microgravity conditions.

flow boiling↗
NASA NTRSDate not supplied

Combining Data with Physical Knowledge for Uncertainty Quantification in Certification and Reliability Analysis

Unifying empirical data with predictive models can enable engineering cost-savings through certification by analysis and reliability-based design. Both concepts require rigorous uncertainty quantification (UQ) and robust understanding and treatment of relevant physics. Combining sampling-based UQ algorithms with high-fidelity simulations creates a computational bottleneck that is often alleviated through the use of machine learning (ML). ML can be used to create computationally efficient surrogates for simulations of complex or high-dimensional physical interactions (e.g., multi-phase interactions associated with melt pools in laser powder bed fusion or spatially-dependent material properties in functionally graded materials). However, negative side effects of ML may include a lack of interpretability and negative correlation between event rarity and simulation accuracy due to a lack of training data. As such, it is important to infuse ML algorithms with physics-based guardrails to provide confidence in their predictions. This talk will provide a brief review of recent NASA research at this intersection of physics-based simulation, ML, and UQ with a focus on certification and reliability analysis.

uncertainty quantification↗
NASA NTRS2023

Advancing Solar and Heliospheric Science Through the Ongoing Development and Support of Atomic and Laboratory Plasma Physics

This paper outlines the necessity for the availability, accessibility, and expansion of atomic physics data and analysis tools for the meaningful interpretation of spectroscopic and polarimetric observations. As we move towards observing the Sun at higher spatio-temporal resolutions, and near-continuously at a range of wavelengths, it becomes critical to develop the appropriate atomic data and physics tools to facilitate scientific progress. We recommend the continued improvement and expansion of current databases to support the development of optically-thick/radiative transfer models, evaluate non-thermal and non-equilibrium ionization effects, and quantify uncertainties in atomic and molecular values. A critical longterm goal will require extending and strengthening collaborations across the atomic, solar/heliospheric, and laboratory plasma physics communities through the participation and training of early career scientists. We also recommend establishing funding for a centralized atomic physics resource made up of a comprehensive and user-oriented atomic database and modeling framework.

Y J Rivera↗
NASA NTRS2024

Thermal Actuator Identification and Control for Thermomechanical Real-Time Cyber–Physical Testing

Thermomechanical cyber–physical testing enables two-way thermal coupling between a numerical and an experimental subsystem. The interactions between the numerical model and the physical specimen occur through transfer systems, which enforce interface conditions. Thus, efficient control methodologies are necessary to achieve the desired interface interaction through thermal actuators with minimal error. This study introduces a novel thermal transfer system that imposes distributed cooling (or heating) thermal loads on a physical subsystem. First, the thermal actuator is identified considering switching-mode continuous dynamics for heating and cooling conditions. A switching-mode estimation algorithm is adopted to estimate the operating thermal cycle of the actuator in real-time. A control system is developed to experimentally impose the desired temperature and reduce tracking error (i.e., the error between the desired and actual temperature) under different thermal cycles. The identification and control of the thermal transfer system are then validated through a set of experiments considering different temperature rates of change. The developed control system is found to effectively minimize tracking errors in real-time cyber–physical experiments.

Herta Montoya↗
NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗
NASA NTRS2023

Cryogenic Flow Boiling in Microgravity: Effects of Reduced Gravity on Two-Phase Fluid Physics and Heat Transfer

With the growing interest in space exploration, cryogenic technologies involving two-phase flow and heat transfer are in high demand to successfully procure advanced space applications such as fuel depots and nuclear thermal propulsion (NTP) systems for deep space missions. However, the unique and extreme thermal properties of cryogenic fluids introduce distinct flow boiling fluid physics and energy transport phenomena, which differ significantly from those observed with conventional fluids. Understanding the unique two-phase physics in cryogenic flow boiling remains an ongoing challenge. Furthermore, the lack of readily available microgravity cryogenic steady-state heat transfer data hinders the assessment of gravitational effects on cryogenic flow boiling. This study aims to elucidate the gravitational effects on two-phase fluid physics and heat transfer by conducting the first-ever experimental measurement of cryogenic flow boiling performance using a steady-state heated method in a reduced gravity environment. Parabolic flight experiments were performed to acquire both heat transfer measurements and high-speed video of interfacial behaviors, under varying gravity levels (microgravity, hypergravity, Lunar gravity, and Martian gravity). The experiments involved flow boiling of liquid nitrogen (LN 2 ) with a near-saturated inlet along a circular heated tube of dimensions 8.5-mm inner diameter and 680-mm heated length. The operating parameters varied are mass velocity of 398.3 - 1342.8 kg/m2s, inlet quality of -0.08 to -0.01, and inlet pressure of 413.68 - 689.48 kPa. Captured microgravity flow patterns range from bubbly to annular, all having vapor structures that are larger than those under higher gravity levels. Under microgravity, absence of buoyancy yields symmetrical vapor structures without flow stratification, laying a physical foundation for the distinct two-phase heat transfer trends during LN 2 flow boiling in microgravity. Transient data collected during the flight parabolas exhibited decreasing heated wall temperature as the aircraft transitioned from hypergravity to microgravity phases. The temperature variation indicated an enhancement in flow boiling heat transfer with decreasing gravity levels and a reduction with increasing gravity levels. The effect of reduced gravity on cryogenic flow boiling heat transfer coefficient (HTC) is discussed based on steady state heat transfer analysis. Seminal HTC correlations are evaluated against the measured microgravity HTC data, of which one is identified for superior accuracy in predicting microgravity data. Finally, a new HTC correlation is proposed to improve accuracy of microgravity predictions, yet there still exists room for further improvement with future terrestrial flow boiling experiments at different flow orientations relative to Earth gravity.

Microgravity↗
DOE OSTI2012

Surface Radiation from GOES: A Physical Approach; Preprint

Models to compute Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) have been in development over the last 3 decades. These models can be classified as empirical or physical, based on the approach. Empirical models relate ground based observations with satellite measurements and use these relations to compute surface radiation. Physical models consider the radiation received from the earth at the satellite and create retrievals to estimate surface radiation. While empirical methods have been traditionally used for computing surface radiation for the solar energy industry the advent of faster computing has made operational physical models viable. The Global Solar Insolation Project (GSIP) is an operational physical model from NOAA that computes GHI using the visible and infrared channel measurements from the GOES satellites. GSIP uses a two-stage scheme that first retrieves cloud properties and uses those properties in a radiative transfer model to calculate surface radiation. NREL, University of Wisconsin and NOAA have recently collaborated to adapt GSIP to create a 4 km GHI and DNI product every 30 minutes. This paper presents an outline of the methodology and a comprehensive validation using high quality ground based solar data from the National Oceanic and Atmospheric Administration (NOAA) Surface Radiation (SURFRAD) (http://www.srrb.noaa.gov/surfrad/sitepage.html) and Integrated Surface Insolation Study (ISIS) http://www.srrb.noaa.gov/isis/isissites.html), the Solar Radiation Research Laboratory (SRRL) at National Renewable Energy Laboratory (NREL), and Sun Spot One (SS1) stations.

14 SOLAR ENERGY↗
DOE OSTI2011

GKTC ACTIVITIES TO PROVIDE NUCLEAR MATERIAL PHYSICAL PROTECTION, CONTROL AND ACCOUNTING TRAINING FOR 2011-2012

The GKTC was created at the Kyiv Institute of Nuclear Research as a result of collaborative efforts between the United States and Ukraine. The GKTC has been designated by the Ukrainian Government to provide the MPC&A training and methodological assistance to nuclear facilities and nuclear specialists. In 2010 the GKTC has conducted the planned assessment of training needs of Ukrainian MPC&A specialists. The objective of this work is to acquire the detailed information about the number of MPC&A specialists and guard personnel, who in the coming years should receive the further advanced training. As a result of the performed training needs evaluation the GKTC has determined that in the coming years a number of new training courses need to be developed. Some training courses are already in the process of development. Also taking into account the specific of activity on the guarding of nuclear facilities, GKTC has begun to develop the specialized training courses for the guarding unit personnel. The evaluation of needs of training of Ukrainian specialists on the physical protection shows that without the technical base of learning is not possible to satisfy the needs of Ukrainian facilities, in particular, the need for further training of specialists who maintains physical protection technical means, provides vulnerability assessment and testing of technical means. To increase the training effectiveness and create the basis for specialized training courses holding the GKTC is now working on the construction of an Interior (non-classified) Physical Protection Training Site. The objective of this site is to simulate the actual conditions of the nuclear facility PP system including the complex of engineering and technical means that will help the GKTC training course participants to consolidate the knowledge and gain the practical skills in the work with PP system engineering and technical means for more effective performance of their official duties. This paper briefly describes the practical efforts applied to the provision of physical protection specialists advanced training in Ukraine and real results on the way to implement such efforts in 2011-2012.

Romanova, Olena↗
DOE OSTI2013

Hardware device to physical structure binding and authentication

Detection and deterrence of device tampering and subversion may be achieved by including a cryptographic fingerprint unit within a hardware device for authenticating a binding of the hardware device and a physical structure. The cryptographic fingerprint unit includes an internal physically unclonable function ("PUF") circuit disposed in or on the hardware device, which generate an internal PUF value. Binding logic is coupled to receive the internal PUF value, as well as an external PUF value associated with the physical structure, and generates a binding PUF value, which represents the binding of the hardware device and the physical structure. The cryptographic fingerprint unit also includes a cryptographic unit that uses the binding PUF value to allow a challenger to authenticate the binding.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Secure control systems with application to cyber-physical systems

Control systems are computer-based systems with networked units consisting of sensors, actuators, control processing units, and communication devices. The role of control system is to interact, monitor, and control physical processes. Reactive power control is a fundamental issue in ensuring the security of the power network. It is claimed that Synchronous Condensers (SC) have been used at both distribution and transmission voltage levels to improve stability and to maintain voltages within desired limits under changing load conditions and contingency situations. Performance of PI controller corresponding to various tripping faults are analyzed for SC systems. Most of the eort in protecting these systems has been in protection against random failures or reliability. However, besides failures these systems are subject to various signal attacks for which new analysis are discussed here. When a breach does occur, it is necessary to react in a time commensurate with the physical dynamics of the system as it responds to the attack. Failure to act swiftly enough may result in undesirable, and possibly irreversible, physical eects. Therefore, it is meaningful to evaluate the security of a cyber-physical system, especially to protect it from cyber-attack. Illustrative numerical examples are provided together with an application to the SC systems.

Dong, Jin↗
DOE OSTI2016

Workshop on Physics with Neutral Kaon Beam at JLab (KL2016) Mini-Proceedings

The KL2016 Workshop is following the Letter of Intent LoI12-15-001 "Physics Opportunities with Secondary KL beam at JLab" submitted to PAC43 with the main focus on the physics of excited hyperons produced by the Kaon beam on unpolarized and polarized targets with GlueX setup in Hall D. Such studies will broaden a physics program of hadron spectroscopy extending it to the strange sector. The Workshop was organized to get a feedback from the community to strengthen physics motivation of the LoI and prepare a full proposal.

Strakovsky, Igor I.↗
DOE OSTI2016

A Software Toolkit to Study Systematic Uncertainties of the Physics Models of the Geant4 Simulation Package

The Geant4 toolkit is used to model interactions between particles and matter. Geant4 employs a set of validated physics models that span a wide range of interaction energies. These models are tuned to cover a large variety of possible applications. This raises the critical question of what uncertainties are associated with the Geant4 physics model, or group of models, involved in a simulation project. To address the challenge, we have designed and implemented a comprehen- sive, modular, user-friendly software toolkit that allows the variation of one or more parameters of one or more Geant4 physics models involved in simulation studies. It also enables analysis of multiple variants of the resulting physics observables of interest in order to estimate the uncertain- ties associated with the simulation model choices. Key functionalities of the toolkit are presented in this paper and are illustrated with selected results.

Genser, Krzysztof↗
DOE OSTI2017

Can We Practically Bring Physics-based Modeling Into Operational Analytics Tools?

We present that analytics software is increasingly used to improve and maintain operational efficiency in commercial buildings. Energy managers, owners, and operators are using a diversity of commercial offerings often referred to as Energy Information Systems, Fault Detection and Diagnostic (FDD) systems, or more broadly Energy Management and Information Systems, to cost-effectively enable savings on the order of ten to twenty percent. Most of these systems use data from meters and sensors, with rule-based and/or data-driven models to characterize system and building behavior. In contrast, physics-based modeling uses first-principles and engineering models (e.g., efficiency curves) to characterize system and building behavior. Historically, these physics-based approaches have been used in the design phase of the building life cycle or in retrofit analyses. Researchers have begun exploring the benefits of integrating physics-based models with operational data analytics tools, bridging the gap between design and operations. In this paper, we detail the development and operator use of a software tool that uses hybrid data-driven and physics-based approaches to cooling plant FDD and optimization. Specifically, we describe the system architecture, models, and FDD and optimization algorithms; advantages and disadvantages with respect to purely data-driven approaches; and practical implications for scaling and replicating these techniques. Finally, we conclude with an evaluation of the future potential for such tools and future research opportunities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗