A Transfer Learning Framework for Power System Event Identification
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24 POWER TRANSMISSION AND DISTRIBUTION↗
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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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Investigation of the effects of passive pre-chamber nozzle pattern and ignition system on engine performance and emissions
The impact of passive pre-chamber (PC) internal volume, nozzle hole pattern (i.e. with and without a central axial nozzle), and PC igniter plug type on performance and emissions was investigated in an optically accessible, single-cylinder, gasoline research engine. The two PC igniter plugs investigated were a conventional inductive coil spark plug and a nanosecond repetitively pulsed (NRP) plasma discharge system previously demonstrated to accelerate early flame propagation. The baseline PC design featured a funnel shaped internal volume with a PC tip that contained six radial nozzles and a larger central axial nozzle. Two additional PC tip geometries were evaluated where either the baseline internal volume was increased or the axial nozzle was removed and the radial nozzle diameters were increased. A sweep of charge equivalence ratios ( ϕ) from nearly stoichiometric to the lean limit was performed for a fixed engine speed (1300 revolutions per minute), and engine load (3.5 bar gross indicated mean effective pressure). Time-resolved PC and main chamber (MC) pressure data as well as MC emissions data were collected to analyze engine performance and emissions characteristics. Combustion in the MC was further investigated using high-speed excited methylidyne radical (CH*) chemiluminescence imaging. Collected results highlighted that while all PC tips and ignition systems exhibited similar performance and emissions down to ϕ = 0.8, relevant differences in thermal efficiency and emissions for leaner charge mixtures were observed, with the results highly dependent on the nozzle pattern and ignition system. Major deviations were correlated to preferential de-pressurization of the PC through the axial nozzle for lean conditions that was not observed for mixture conditions closer to stoichiometric. Results show that a combination of radial and axial nozzle patterns in the PC extended lean-stability limits at the low-load condition evaluated. Further benefits were observed with the use of NRP ignition systems due to faster combustion within the PC volume provided that the volume was sufficiently large.
Di Sabatino, Francesco↗
Processing ultrasonic inspection data from multiple scan patterns for turbine rotor weld build-up evaluations
The study presents a data processing methodology for weld build-up using multiple scan patterns. To achieve an overall high probability of detection for flaws with different orientations, an inspection procedure with three different scan patterns is proposed. The three scan patterns are radial-tangential longitude wave pattern, axial-radial longitude wave pattern, and tangential shear wave pattern. Scientific fusion of the inspection data is implemented using volume reconstruction techniques. The idea is to perform spatial domain forward data mapping for all sampling points. A conservative scheme is employed to handle the case that multiple sampling points are mapped to one grid location. The scheme assigns the maximum value for the grid location to retain the largest equivalent reflector size for the location. The methodology is demonstrated and validated using a realistic ring of weld build-up. Tungsten balls and bars are embedded to the weld build-up during manufacturing process to represent natural flaws. Flat bottomed holes and side drilled holes are installed as artificial flaws. Automatic flaw identification and extraction are demonstrated. Results indicate the inspection procedure with multiple scan patterns can identify all the artificial and natural flaws.
42 ENGINEERING↗
Tetris-inspired detector with neural network for radiation mapping
Abstract Radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. Regarding materials and their configurations, radiation detectors have been developed to identify the position and strength of the radioactive sources. However, due to the complex mechanisms of radiation-matter interaction and data limitation, high-performance and low-cost radiation mapping is still challenging. Here, we present a radiation mapping framework using Tetris-inspired detector pixels. Applying inter-pixel padding for enhancing contrast between pixels and neural networks trained with Monte Carlo (MC) simulation data, a detector with as few as four pixels can achieve high-resolution directional prediction. A moving detector with Maximum a Posteriori (MAP) further achieved radiation position localization. Field testing with a simple detector has verified the capability of the MAP method for source localization. Our framework offers an avenue for high-quality radiation mapping with simple detector configurations and is anticipated to be deployed for real-world radiation detection.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Bridging the length scales on mechanical property evaluation (Final Report)
The development of small-scale mechanical testing in combination with microstructural investigation is of great interest to the nuclear materials community for both materials development and monitoring applications. Dramatically reducing the sample sizes to reduce radioactivity and obtaining mechanical properties of irradiated samples is truly intriguing. Moreover, such studies promise a range of benefits including cost reduction, fundamental insight in structure-property relationships, increased statistics on less sample material, and reinvestigation of prior irradiated and tested reactor samples while simultaneously enabling the generation of mechanical test data on ion beam irradiated materials with limited penetration depths. Small scale materials testing on sub-sized samples has been studied for several decades, though it has only been after the development of micro-testing based on Focused Ion Beam (FIB) sample manufacturing in that orders of magnitude smaller samples could really be investigated in a quantitative manner. In recent years, small scale mechanical testing techniques at a number of length scales has been developed for both unirradiated and irradiated (ion and neutron) materials. Technological advances made in this field have enabled ex-situ and in-situ transmission electron microscopy (TEM) and scanning electron microscopy (SEM) examination, thus leading to more accurate measurements as well as additional mechanistic information. A recent review of the benefits of these techniques show that these techniques are at a stage to tackle multi-scale ranges of materials investigations and can be utilized to obtain fundamental science-based understanding of nuclear materials. Considering the tremendous advances made, one can see how small-scale mechanical testing techniques combined with modeling can enable true small scale to bulk scale mechanical property correlations. However, for the engineering community to adapt this approach fully, one needs to demonstrate that a) that these techniques can produce results with high fidelity and reproducibility, b) generate engineering stress-strain data that one can utilize to understand bulk behavior, and c) generate new insight into relevant phenomena fostering the true understanding of radiation damage and microstructure in materials for nuclear applications. It is the objective of this proposal to bridge the length scale between macro- and micro- scale mechanical testing of unirradiated and irradiated materials. This involves the development and demonstration of procedures for multi-scale mechanical testing that enable high fidelity reproducibility of data and the generation bulk property data from small-scale mechanical tests. Through this, the proposal aims to enhance the confidence in the obtained data at the smaller length scales and enhance the insight provided from these techniques for bulk scale applications on both unirradiated and irradiated materials.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Expanded analysis of machine learning models for nuclear transient identification using TPOT
Industries around the world are becoming more and more data driven. The nuclear field is no exception with several different applications being proposed. One popular area of research is the use of machine learning in transient detection. This paper seeks to build upon a previous study which made use of the AutoML package TPOT to train traditional machine learning models to classify transient events occurring with a reactor. Synthetic data was once again collected using a GPWR reactor simulator. Data on 12 different events was collected using 15 different initial conditions. Here, a dataset consisting of over 100,000 data points was compiled and used to train 7 different machine learning models using a pre-defined TPOT dictionary with 12 different preprocessing techniques. Three of the trained models were able to produce validation results in the 90s with the expanded dataset. Once the models were trained, it was possible to look into where during the simulation, misclassifications occurred. Using these three models, analysis was done to determine if TPOT could be used to train models that were effective if important features were missing. The results from this were positive with the newly trained models scoring close to the original models. Finally, to conclude this study, the three high performing models were retrained using different random states to see if there was any major variation when different states were used.
42 ENGINEERING↗
Hydropower Resilience Database for Assessing Microgrid Formation Capability and Enhancing Power Grid Resilience
In the face of increasing frequency of extreme events, enhancing resilience and reliability of energy infrastructure demands innovative solutions. Hydropower, with its inherent generation flexibility and grid-forming capability, offers significant potential for enhancing power grid resilience through the establishment of microgrids. To enable informed decision-making and strategic planning, we present the Hydropower Resilience Database (HRD) that integrates data from various sources such as Oakridge National Laboratory (ORNL) HydroSource, National Inventory of Dams, and US Western Grid database. By integrating relevant information on hydropower plant characteristics, including dams, reservoirs, and connected electrical grid, this resource enables hydropower plant owners, utilities, and community stakeholders to identify and evaluate the feasibility of using hydropower resources in microgrids to support nearby communities and critical infrastructure in various challenging scenarios. Using HRD, a set of metrics are evaluated to quantify the capability of hydropower plants to support essential microgrid functions. An interactive tool is developed using ArcGIS, enabling the visualization and analysis using HRD. Our research contributes to strategic planning efforts for fortifying energy infrastructure, ensuring reliable power supply during disruptions, and advancing the development of robust and resilient energy systems in regions susceptible to grid vulnerabilities.
13 HYDRO ENERGY↗
Hydropower Resilience Database for Assessing Microgrid Formation Capability and Enhancing Power Grid Resilience
In the face of increasing frequency of extreme events, enhancing resilience and reliability of energy infrastructure demands innovative solutions. Hydropower, with its inherent generation flexibility and grid-forming capability, offers significant potential for enhancing power grid resilience through the establishment of microgrids. To enable informed decision-making and strategic planning, we present the Hydropower Resilience Database (HRD) that integrates data from various sources such as Oakridge National Laboratory (ORNL) HydroSource, National Inventory of Dams, and US Western Grid database. By integrating relevant information on hydropower plant characteristics, including dams, reservoirs, and connected electrical grid, this resource enables hydropower plant owners, utilities, and community stakeholders to identify and evaluate the feasibility of using hydropower resources in microgrids to support nearby communities and critical infrastructure in various challenging scenarios. Using HRD, a set of metrics are evaluated to quantify the capability of hydropower plants to support essential microgrid functions. An interactive tool is developed using ArcGIS, enabling the visualization and analysis using HRD. Our research contributes to strategic planning efforts for fortifying energy infrastructure, ensuring reliable power supply during disruptions, and advancing the development of robust and resilient energy systems in regions susceptible to grid vulnerabilities.
13 HYDRO ENERGY↗
Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance
Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.
42 ENGINEERING↗
Final Technical Report: Moving ASR Cloud Microphysical Retrievals Beyond the Vertical Column
Clouds of all types play important roles in atmospheric heating, surface heating and emission of radiation, hence energy, to space. Clouds not only affect atmospheric heating via their impacts on solar and terrestrial radiation, they also release kinetic energy to the atmosphere when they form and they extract kinetic energy from the atmosphere when they evaporate. To understand the impacts of clouds on weather and climate, one must be able to make accurate observations of the amount of water associated with them and the impact of this water on atmospheric and surface heating rates. One important cloud type is liquid water clouds. Liquid clouds are often found in the boundary layers of Earth at altitudes from just above the surface to approximately 2000 m above it, at a variety of temperatures, including temperatures below the freezing point of liquid water (0 °C). As a result, they are important constituents over the world’s oceans as well as over cold, ice-covered surfaces such as in the Arctic. Observationally estimating the total amount of liquid contained in a column through a liquid water cloud can now be accomplished to acceptable accuracies (except for the thinnest, lowest liquid containing clouds) using instruments called passive microwave radiometers. However, observationally determining the spatial distribution of liquid within these columns has proven to be difficult. The focus of this project was on the application of two radars operating at two different frequencies to retrieve the distribution of liquid water within columns passing through liquid water clouds. Motivation in this project for using two different radars operating at different frequencies is that as overlapping (in space) beams from the two radars pass through the same column of liquid water cloud absorption of power from one beam (the 94-GHz beam) is greater than absorption of power from the other beam (the 35-GHz beam). Therefore, the difference in returned powers between the two radar beams with distance from the radars is proportional to the amount of liquid water that the two beams have passed through. Therefore, one can use the differences in power between the two beams with distance through a liquid water cloud to determine the distribution of liquid water throughout the cloud. The two radars used in this study are the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program Ka-band Scanning ARM Cloud Radar (Ka-SACR) and the W-band Scanning ARM Cloud Radar (W-SACR). When this projected started in 2013, the Ka- and W-band SACRs were new technology, state-of-the-art sensors. To put together a sufficiently diverse and big set of test cases for the study, data from the SACRs deployed to NSA, SGP, TCAP and MAGIC were used, leading to a test set composed of 65 hours of suitable data spread over approximately 17 case study days. Using the Ka- and W-band SACR data from the case study periods, vertical distributions of liquid water content through liquid water clouds were retrieved. Because there are no other data sets with which to compare the retrievals, distributions of liquid water through a column were summed and compared to the total amounts of liquid water obtained by retrievals applied to passive microwave radiometer observations. The total amounts of liquid water (in units of millimeters, or the thickness in millimeters of the total liquid water in a vertical column if allowed to pool at the bottom of the column) between the two retrievals agreed to within 0.060 mm with a standard deviation of 0.31 mm, or to within 0.016 mm with a standard deviation of 0.12 mm once outliers were removed. The standard deviation of 0.12 mm was fairly independent of the total amount of liquid water in the clouds, indicating that retrieval errors were larger for clouds with smaller amounts of liquid in them. Errors in the retrieved total amounts of liquid were also found to be sensitive to the a priori information that was fed into the retrieval approach. Overall, standard deviations of 0.12 mm are sufficient for some cloud studies but for radiation studies standard deviations on the order of 0.02 mm need to be achieved for clouds with the lowest amounts of liquid water. Many modifications to the retrieval approach were tested subsequently in an attempt to reduce the standard deviations relative to the radiometer retrievals below 0.12 mm but none of them worked reliably. We decided to wait until the DOE ARM Program deployed its second generation SACR2s to its NSA and ENA sites for further retrieval evaluation. The SACR2s were engineered to better synchronize data between the Ka- and W-band radars, reducing potential sources of noise for radar retrieval of liquid water distributions within clouds. The SACR2s have improved in reliability recently and the system deployed to ENA is best positioned to provide the data to advance research on this path. This will be an ongoing research activity beyond the project because of the promise in SACR2 data from the DOE ARM ENA Facility.
58 GEOSCIENCES↗
Frequency Injection Based HVDC Attack-Defense Control Via Squeeze-Excitation Double CNN
Due to the independent controllability and fast power regulation capability, the High Voltage Direct Current (HVDC) system could be a prospective technology to provide multiple ancillary services to the system besides conventional bulk power transmission. However, with the increase of False Data Injection Attacks (FDIAs) on PMU data, the HVDC system could have the wrong response once the collected data that the HVDC system relied on is attacked, thus threatening the system operating security. How to ensure the security of the PMU-based HVDC ancillary service control become an urgent issue. To mitigate the risk, this paper proposed an HVDC attack-defense control based on the FDIAs detection method. Firstly, the Squeeze-Excitation based Double Convolutional Neural Networks (SE-DCNN) is proposed to realize fast identification of the attacking frequency type based on the time and frequency domain signals. Furthermore, the duration time of FDIAs is detected by the local outlier factor. Then, utilizing the results from SE-DCNN, HVDC ancillary service control framework is reorganized and an HVDC attack defense control is proposed for suppressing the potential influence of various types of FDIAs on the HVDC system ancillary service. Different experiments results demonstrate that the proposed method has the ability to significantly mitigate the frequency deviation and oscillation under the FDIA.
42 ENGINEERING↗
Block-Structured Operator Inference for Coupled Multiphysics Model Reduction
This work presents a block-structured formulation of Operator Inference as a way to learn structured reduced-order models for multiphysics systems. The approach specifies the governing equation structure for each physics component and the structure of the coupling terms. Once the multiphysics structure is specified, the reduced-order model is learned from snapshot data following the nonintrusive Operator Inference methodology. In addition to preserving physical system structure, which in turn permits preservation of system properties such as stability and second-order structure, the block-structured approach has the advantages of reducing the overall dimensionality of the learning problem and admitting tailored regularization for each physics component. The numerical advantages of the block-structured formulation over a monolithic Operator Inference formulation are demonstrated for aeroelastic analysis, which couples aerodynamic and structural models. For the benchmark test case of the AGARD 445.6 wing, block-structured Operator Inference provides an average 20% online prediction speedup over monolithic Operator Inference across subsonic and supersonic flow conditions in both the stable and fluttering parameter regimes while preserving the accuracy achieved with monolithic Operator Inference.
42 ENGINEERING↗
VERA and VERA-EDU 3.5 Release Notes
The Virtual Environment for Reactor Applications components included in this distribution include selected computational tools and supporting infrastructure that solve neutronics, thermal-hydraulics, fuel performance, and coupled neutronics-thermal hydraulics problems. The infrastructure components provide a simplified common user input capability and provide for the physics integration with data transfer and coupled-physics iterative solution algorithms. Neutronics analysis can be performed for 2D lattices, 2D core and 3D core problems for pressurized water reactor geometries that can be used to calculate criticality and fission rate distributions by pin for input fuel compositions. MPACT uses the Method of Characteristics transport approach for 2D problems. For 3D problems, MPACT uses the 2D/1D method which uses 2D MOC in a radial plane and diffusion or SPn in the axial direction. MPACT includes integrated cross section capabilities that provide problem-specific cross sections generated using the subgroup methodology. The code can be executed both 2D and 3D problems in parallel to reduce overall run time. A thermal-hydraulics capability is provided with CTF (an updated version of COBRA-TF) that allows thermal-hydraulics analyses for single and multiple assemblies using the simplified VERA common input. This distribution also includes coupled neutronics/thermal-hydraulics capabilities to allow calculations using MPACT coupled with CTF. The VERA fuel rod performance component BISON calculates, on a 2D or 3D basis, fuel rod temperature, fuel rod internal pressure, free gas volume, clad integrity and fuel rod waterside diameter. These capabilities allow simulation of power cycling, fuel conditioning and deconditioning, high burnup performance, power uprate scoping studies, and accident performance. Input/Output capabilities include the VERA Common Input (VERAIn) script which converts the ASCII common input file to the intermediate XML used to drive all of the physics codes in the VERA Core Simulator (VERA-CS). VERA component codes either input the VERA XML format directly, or provide a preprocessor which can convert the XML into native input. VERAView is an interactive graphical interface for the visualization and engineering analyses of output data from VERA. The python-based software is easy to install and intuitive to use, and provides instantaneous 2D and 3D images, 1D plots, and alpha-numeric data from VERA multi-physics simulations. Testing within CASL has focused primarily on Westinghouse four-loop reactor geometries and conditions with example problems included in the distribution.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Data-Driven Control: Theory and Applications
The ushering in of the big-data era, ably supported by exponential advances in computation, has provided new impetus to data-driven control in several engineering sectors. This topic's rapid and deep expansion has precipitated the need to showcase the highlights of data-driven approaches. There has been a rich history of contributions from the control systems community in data-driven control. At the same time, several new concepts and research directions have also been introduced in recent years. Many of these contributions and concepts have started to transition from theory to practical applications. This paper will overview the historical contributions and highlight recent concepts and research directions.
Soudbakhsh, Damoon↗
Oscilloscope Data Push Program
This paper details the development of a Python program designed to automate the data acquisition and conversion for an oscilloscope for the purposes of a one-off/temporary data acquisition system for users that readily need data, and do not have the option of obtaining a Data Acquisition (DAQ) solution. Creating DAQ systems for analyzing a system requires expensive electronics and a dedicated team of engineers for support. Traditionally, manual data collection and processing are time consuming and prone to error. By automating these processes, the cost, efficiency and accuracy of data handling are improved upon. This project involves the creation of a program that interacts with the oscilloscope. During this interaction, there are various functions being performed such as the acquisition of waveform data via floating points, generating plots with the acquired wave points, and storing of floating points in a CSV file format for future reference and plotting purposes. While the initial aim of the project included continuous logging to a cloud database, this was deferred due to time constraints. The results portrayed an almost-instant rate of data collection with a buffer time, showcasing the potential for further integration and real-time data processing.
Osei-Tutu, Jason↗
Evaluation of a scientific data search infrastructure
The ability to search over large scientific datasets has become crucial to next-generation scientific discoveries as data generated from scientific facilities grow dramatically. In previous work, we developed and deployed ScienceSearch, a search infrastructure for scientific data which uses machine learning to automate metadata creation. Our current deployment is deployed atop a container based platform at a HPC center. In this article, we present an evaluation and discuss our experiences with the ScienceSearch infrastructure. Specifically, we present a performance evaluation of ScienceSearch's infrastructure focusing on scalability trends. The obtained results show that ScienceSearch is able to serve up to 130 queries/min with latency under 3 s. We discuss our infrastructure setup and evaluation results to provide our experiences and a perspective on opportunities and challenges of our search infrastructure.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking
The intermittency of carbon-free renewables and the demand changes associated with the widespread push for electrifying the transportation and building sectors provides an opportunity for buildings to go beyond energy efficiency and push towards providing demand flexibility to the electricity grid. The duality of energy efficiency and demand flexibility is necessary for success in a sustainable and reliable energy transition. Current building energy benchmarking models are limited in their ability to integrate concepts of demand flexibility and/or utilize granular smart meter data. Thus, current benchmarking methods are focused annual energy usage and fail to incorporate how the time of use of energy consumption impacts emissions in a quickly changing energy grid. Without a more comprehensive view of energy usage and associated real-time emissions, current benchmarking methods are unlikely to realize the full decarbonization potential of buildings. New emerging data streams provide an opportunity to develop a new generation of benchmarking energy models that embed dimensions of energy efficiency, grid interactivity, and demand flexibility into their analysis. In this paper, we propose a four-step method for embedding grid interactivity and demand flexibility into building benchmarking models that utilizes emerging building and time-series electricity data streams. We first engineer features to produce a mix-type dataset that encompasses many attributes of grid-interactive and efficient buildings, and then we apply K-medoids using Gower's Distance to produce peer-group clusters. We apply the method to a case study of 306 primary and secondary schools in southern California, USA. The results show that the method effectively clusters buildings by attributes of demand flexibility and energy efficiency. The clustering results reveal patterns in inefficient building operations and demand inflexibility at the building peer group level. In conclusion, the interpretation of clusters can serve as an integrated energy efficiency and demand flexibility benchmarking model and inform performance-specific policy targeting for buildings that go beyond traditional efficiency measures.
24 POWER TRANSMISSION AND DISTRIBUTION↗
An integrated environmental and health performance quantification model for pre-occupancy phase of buildings in China
To comprehensively pre-evaluate the damages to both the environment and human health due to construction activities in China, this paper presents an integrated building environmental and health performance (EHP) assessment model based on the Building Environmental Performance Analysis System (BEPAS) and the Building Health Impact Analysis System (BHIAS) models and offers a new inventory data estimation method. The new model follows the life cycle assessment (LCA) framework and the inventory analysis step involves bill of quantity (BOQ) data collection, consumption data formation, and environmental profile transformation. The consumption data are derived from engineering drawings and quotas to conduct the assessment before construction for pre-evaluation. The new model classifies building impacts into three safeguard areas: ecosystems, natural resources and human health. Thus, this model considers environmental impacts as well as damage to human wellbeing. The monetization approach, distance-to-target method and panel method are considered as optional weighting approaches. Finally, nine residential buildings of different structural types are taken as case studies to test the operability of the integrated model through application. The results indicate that the new model can effectively pre-evaluate building EHP and the structure type significantly affects the performance of residential buildings.
54 ENVIRONMENTAL SCIENCES↗