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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Parametric, Data-Driven, Non-Intrusive Reduced-Order Model Framework for Crystal Plasticity Simulations of Voids

The influence of the internal structure at micrometer length scales on the deformation of polycrystalline materials can be effectively captured using crystal plasticity finite element methods (CPFEM). However, the complexity and nonlinearity of the deformation equations CPFEM solves demand significant computational power and resources to achieve accurate predictions, limiting its broader application. To address this challenge, we have identified a reduced-order representation of the complex data in order to establish a computationally efficient reduced-order models (ROM) and drastically reduce the computational expense of CPFEM. Specifically, in this work, we developed a parametric, data-driven, and non-intrusive ROM framework for CPFEM using proper orthogonal decomposition (POD) and sparse variational Gaussian process (SVGP) regression for single-crystal microstructures under tensile loading conditions. The developed protocol enables one to compress field into a latent/low-dimensional space described by principal component analysis (PCA) via the singular value decomposition (SVD) algorithm. As a result, the high-dimensional data are reduced to a significantly smaller amount of dimensions with POD bases and POD coefficients. Furthermore, we deployed an ensemble of SVGPs—extended from the classical Gaussian process (GP) regression for scalability and handling big data—in a massively parallel manner to train and predict latent POD coefficients using known POD bases from a set of previously obtained simulations results. Lastly, using the predicted POD coefficients, we reconstructed the full-field results and showed reasonable agreement compared with the true values obtained from running CPFEM. The developed framework is validated with a set of CPFEM simulations of a single embedded void in single-crystal aluminum alloy. While the framework is broadly applicable, this work specifically focuses on single-crystal microstructures, a single load case (e.g., tensile), and a specific void geometry (spherical).

Anisotropy↗
DOE OSTI2025

Petrology and geochemistry of the Middle Pennsylvanian (Langsettian) Clintwood coalbed, Pike County, Kentucky

Abstract The Middle Pennsylvanian, Langsettian sub-stage Clintwood coal, along with its correlatives, was one of the more important energy resources in eastern Kentucky. The coal thickens from the NE-SW-trending Belfry anticline in central Pike County to the southeast, towards the Virginia-Kentucky border. Much of the thickness increase is in the bright lithotypes below a dull lithology. The bright lithologies transition from a bright clarain + vitrain + fusain lithology to a clarain + fusain lithology to the southeast. Half of the lithotypes in the three analyzed sections exceed 1000-µg/g Rare earth elements + Y + Sc (REYSc) (ash basis). The La N /Sm N vs. Gd N /Yb N and La N /Sm N vs. Ce N /Ce N * plots suggest that the top lithotype may have had best indication of an oxidizing environment. Positive Gd N /Gd N * and Eu N /Eu N * in some of the lithologies suggests that there may have been a hydrothermal influence in the sediment sources and/or in the diagenesis of the coal. Consideration of La N /Sm N and Zr (µg/g; ash basis), Zr vs. Sr, and V/Cr vs. inertinite/(inertinite + vitrinite) indicates that the dull lithotypes existed in a distinct geochemical environment compared to the brighter lithotypes. The isolation of the dull lithotypes from the bright lithotypes is supported by principal components analysis on inertinite/(inertinite + vitrinite), La N /Sm N , V/Cr, Sr, and either Zr or Ln(100(TiO 2 )/Al 2 O 3 ).

Hower, James C. (ORCID:0000000346942776)↗
DOE OSTI2022

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques

Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of CO 2 sequestration). Here, we extend and present a non-intrusive reduced order model of natural convection in porous media employing deep convolutional autoencoders for the compression and reconstruction and either radial basis function (RBF) interpolation or artificial neural networks (ANNs) for mapping parameters of partial differential equations (PDEs) on the corresponding nonlinear manifolds. To benchmark our approach, we also describe linear compression and reconstruction processes relying on proper orthogonal decomposition (POD) and ANNs. Further, we present comprehensive comparisons among different models through three benchmark problems. The reduced order models, linear and nonlinear approaches, are much faster than the finite element model, obtaining a maximum speed-up of 7 × 10 6 because our framework is not bound by the Courant–Friedrichs–Lewy condition; hence, it could deliver quantities of interest at any given time contrary to the finite element model. Our model’s accuracy still lies within a relative error of 7% in the worst-case scenario. We illustrate that, in specific settings, the nonlinear approach outperforms its linear counterpart and vice versa. We hypothesize that a visual comparison between principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) could indicate which method will perform better prior to employing any specific compression strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

58 GEOSCIENCES↗
DOE OSTI2026

Maximizing machine learning interatomic potential transferability for the discovery of the novel stellated octadecagon Bi18-Pt24 cage structure

Achieving true transferability remains the central challenge for Machine Learning Interatomic Potentials (ML-IAPs) in modeling complex bimetallic nanoclusters across their vast potential energy surfaces. We systematically investigate data selection strategies to optimize the Chebyshev Interaction Model for Efficient Simulation (ChIMES) potential for the Bi-Pt nanoclusters by comparing three innovative sampling methods: Principal Component Analysis (PCA)/k-means (structural diversity), t-distributedStochasticNeighborEmbedding (t-SNE)/k-means (force-space diversity), and hierarchical clustering. Quantitatively, the PCA/k-means strategy proved most effective for global accuracy, yielding the lowest force errors and achieving energy root mean square errors (RMSE) values competitive with Density Functional Theory (DFT), demonstrating excellent accuracy (19.16meV/atom). Structural validation on 34 unique DFT-optimized isomers further confirmed the potential’s high fidelity, with the best model PCA/k-means reproducing structures with an average root mean square deviation (RMSD) of 0.10 Å. However, the t-SNE methods, by maximizing diversity in the force space, demonstrated superior extrapolative power, leading to the more precise prediction of a novel stellated octadecagon Bi18⁢Pt24 cage structure, demonstrating the potential for exploring previously unseen morphologies. Our results establish a clear methodology for strategic data sampling that successfully maximizes ML-IAP transferability, providing an accurate and computationally efficient tool that accelerates the theoretical discovery of complex bimetallic architectures.

Vangheluwe, Raphaël [Université Paris-Saclay, CNRS↗
DOE OSTI2022

Sizing ramping reserve using probabilistic solar forecasts: A data-driven method

Ramping products have been introduced or proposed in several U.S. power markets to mitigate the impact of load and renewable uncertainties on market efficiency and reliability. Current methods often rely on historical data to estimate the requirements of ramping products and fail to take into account the effects of the latest weather conditions and their uncertainties, which could lead to overly conservative or insufficient requirements. This study proposes a k-nearest-neighbor-based method to give weather-informed estimates of ramping needs based on short-term probabilistic solar irradiance forecasts. Forecasts from multiple sites are employed in conjunction with principal component analysis to derive numerical classifiers to characterize system-level weather conditions. In addition, we develop a data-driven method to optimize the model parameters in a rolling-forward manner. By using real-world data from the California Independent System Operator, we design two metrics to evaluate method performance: 1) frequency of shortage and 2) oversupply of ramping product. Our proposed method presents advantages in comparison with the baseline and a set of benchmark methods: without compromising system reliability, it reduces system ramping requirements by up to 25%, therefore improving both system reliability and economics.

14 SOLAR ENERGY↗
DOE OSTI2017

Identification of blind geothermal resources in Surprise Valley, CA, using publicly available groundwater well water quality data

Geothermal resource exploration is generally limited to areas with surface expressions of thermal activity (fumaroles and hot springs), or relies on expensive geophysical exploration techniques. The hidden subsurface distribution of geothermal fluids has been identified using a free and publicly available water quality dataset from agricultural and domestic water wells in Surprise Valley, northeastern California. Thermally evolved waters in Surprise Valley have element ratios that vary in response to Ca carbonate and Mg silicate mineral precipitation, and have elevated total dissolved solids (TDS). The arid climate in Surprise Valley leads to surface water evaporation in a closed basin, producing high TDS Na-Cl-CO 3 -SO 4 brines in three ephemeral alkali lakes and in shallow groundwater under elevated soil CO 2 conditions. Evaporated fluids in Surprise Valley follow a chemical divide that leads to Ca carbonate and Mg silicate mineral precipitation. Plots of dissolved element ratios can be used to distinguish groundwater affected by evaporation from water affected by thermal water-rock interaction, however it is challenging to select components for plotting that best illustrate different fluid evolution mechanisms. Here, we use a principal component analysis of centered log-ratio transformed data, coupled with geochemical models of fluid evaporation and thermal mixing pathways, to identify components to plot that distinguish between groundwater samples influenced by evaporation from those influenced by thermal processes. We find that groundwater samples with a thermal signature come from wells that define a coherent, linear geographical distribution that closely matches the location of known and inferred faults. Finally, modification of the general approach employed here provides promise for identifying blind geothermal resources in other locations, by applying low-cost geochemical modeling and statistical techniques to areas where large groundwater quality geochemical datasets are available.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Aspects of rare earth element enrichment in Allegheny Plateau coals, Pennsylvania, USA

The Allegheny Plateau in western Pennsylvania, western Maryland, and northern West Virginia has relatively flat-lying strata with southwest-northeast-trending anticlines cored by splay faults originating from the thrust faults and southeast-northwest-trending lineaments. The lineaments define both the structural relief of the anticlines and the coal rank. Coal rank in the Pennsylvanian Allegheny Group coals is medium volatile bituminous in the region of less-prominent structural relief and low volatile bituminous in the region with more pronounced anticlines. Zinc and Cl exhibit high values in coals in the vicinity of the lineaments. Although coal rank was not a parameter in the Principal components analysis, the highest rank coals clustered together in a quadrant defined, in part, by the Zn concentration. Zinc was selected as a parameter due to its possible link with hydrothermal mineralization and Zr and TiO 2 /Al 2 O 3 were selected based on their presumed association with detrital mineral influences. The highest rare earths + Y values in benches of the Lower Kittanning coal were found at sites close to the lineaments. Negative or negligible Ce anomalies (δCe) and strongly positive Gd anomalies (δGd) suggest marine or hydrothermal influences and negative Eu anomalies (δEu) along with high TiO 2 /Al 2 O 3 values suggest a terrestrial input. In conclusion, the plots of δCe vs. δGd and δCe vs. δEu show that the low volatile bituminous coals are segregated from the other coals, suggesting that the factors that influenced coal rank also influenced the distribution of the rare earth elements.

58 GEOSCIENCES↗
DOE OSTI2024

A survey study on arsenic speciation in coal fly ash and insights into the role of coal combustion conditions

Coal fly ashes (CFAs) are the low-density byproducts of the coal combustion process. Improper or uncontrolled CFA disposal poses significant environmental and health concerns due to the potential leaching of toxic heavy metals such as arsenic (As). Previous studies have investigated the content and speciation of As in different CFA samples, yet systematic information on As speciation in CFA with representative coal source and combustion conditions is still missing. Based on a recent survey study on the typical coal sources and combustion conditions across the U.S., this study selected 19 representative CFA samples to systematically investigate As speciation and potential correlations with these parameters. The composition, morphology, mineralogy, and As speciation of these CFA samples were characterized by complementary analytical, microscopic, and spectroscopic techniques. Synchrotron X-ray spectroscopy and microscopy analyses revealed the dominant As oxidation state to be As(V) and with strong associations to Ca, with the exception of 3 samples that had 19–51% As(III), likely due to the use of selective catalytic reduction (SCR) process. Principal component analysis was conducted to identify potential correlations of As concentration and oxidation state with parameters such as major element content, loss on ignition (LOI), average particle size, coal source, and combustion condition. Al 2 O 3 and FeO content were found to capture a majority of the variability. Further, results from this study provide fundamental basis for understanding the correlations between coal source, combustion conditions, CFA characteristics, and As speciation, and providing insights for downstream beneficial utilization or disposal management.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2015

Anti-pulmonary fibrotic activity of salvianolic acid B was screened by a novel method based on the cyto-biophysical properties

Various methods have been used to evaluate anti-fibrotic activity of drugs. However, most of them are complicated, labor-intensive and lack of efficiency. This study was intended to develop a rapid method for anti-fibrotic drugs screening based on biophysical properties. A549 cells in vitro were stimulated with transforming growth factor-β1 (TGF-β1), and fibrogenesis was confirmed by conventional immunological assays. Meanwhile, the alterations of cyto-biophysical properties including morphology, roughness and stiffness were measured utilizing atomic force microscopy (AFM). It was found that fibrogenesis was accompanied with changes of cellular biophysical properties. TGF-β1-stimulated A549 cells became remarkably longer, rougher and stiffer than the control. Then, the effect of N-acetyl-L-cysteine (NAC) as a positive drug on ameliorating fibrogenesis in TGF-β1-stimulated A549 cells was verified respectively by immunological and biophysical markers. The result of Principal Component Analysis showed that stiffness was a leading index among all biophysical markers during fibrogenesis. Salvianolic acid B (SalB), a natural anti-oxidant, was detected by AFM to protect TGF-β1-stimulated A549 cells against stiffening. Then, SalB treatment was provided in preventive mode on a rat model of bleomycin (BLM) -induced pulmonary fibrosis. The results showed that SalB treatment significantly ameliorated BLM-induced histological alterations, blocked collagen accumulations and reduced α-SMA expression in lung tissues. All these results revealed the anti-pulmonary fibrotic activity of SalB. Detection of cyto-biophysical properties were therefore recommended as a rapid method for anti-pulmonary fibrotic drugs screening. - Highlights: • Fibrogenesis was accompanied with the changes of cyto-biophysical properties. • Cyto-biophysical properties could be markers for anti-fibrotic drugs screening. • Stiffness is a leading index among all biophysical markers. • SalB was detected to protect TGF-β1-stimulated A549 cells against stiffening. • SalB treatment ameliorated pulmonary fibrosis induced by BLM in rats.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Cosolvent enhanced lignocellulosic fractionation tailoring lignin chemistry and enhancing lignin bioconversion

Cosolvent Enhanced Lignocellulosic Fractionation (CELF) is an emerging solvolysis pretreatment to fractionate lignocellulosic biomass. Herein, the bioconversion performance of CELF lignin was fully evaluated for the first time. Results showed that CELF lignin possessed higher content of carboxylic acid OH, lower molecular weight, and disappeared β-O-4 and β-5 linkages compared to other two technical lignins including a conventional ethanol organosolv lignin (EOL) and a kraft lignin (KL). Rhodococcus opacus PD630 cell count from CELF lignin fermentation reached the highest value of 3.9 10 7 CFU/mL, representing a 62.5% and 77.3% improvement over EOL and KL, respectively. Correspondingly, lipid yield reached 143 mg/L from CELF lignin, which was 36.2% and 26.5% higher than from EOL and KL, respectively. Principal component analysis (PCA) revealed that more carboxylic acid groups and lower molecular weight contributed to the enhanced bioconversion performance of CELF lignin. This study demonstrates that CELF lignin is a promising candidate for bioconversion.

09 BIOMASS FUELS↗
DOE OSTI2019

Interpreting mixing relationships in energetic melts to estimate vapor contribution and composition

Heterogeneous, glassy debris can be produced by energetic events such as near surface nuclear explosions and asteroid impacts. In these events, the resultant debris comprises a rapidly-cooled mixture of the projectile or bomb material with proximate materials such as soil and anthropogenic structures. Glasses formed in these events preserve inter and/or intra sample compositional variation reflecting complex mixtures of the precursor materials or their chemically fractionated products. Understanding formation mechanisms and chemical processes relating to energetic glass formation based on study of the end products requires an objective method for calculating the precursor components that mixed together to produce the final product. Here, we compare a set of multivariate linear-least squares approaches to address the unmixing problem of heterogeneous glasses including classical least squares (CLS), principal component analysis (PCA) and two multivariate curve resolution – alternating least squares (MCR-ALS) approaches. We generate a synthetic data set representing spatially resolved, compositional data in a set of heterogeneously mixed glasses to understand model sensitivity to precursor abundance and composition. We explore the impact of incomplete knowledge of the system prior to the formation of the melts on the resulting confidence and accuracy of the solution (i.e., the number, composition, and abundances of the precursor materials). We conclude that a closure and equality constrained MCR-ALS approach succeeds in identifying unrecognized precursor components and is a suitable alternative to CLS approaches when the investigator has incomplete knowledge of the starting precursor chemical compositions. Finally, we demonstrate application of this MCR-ALS approach to measurements of Trinitite.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2019

Surface evolution of synthetic bilgewater emulsion

Bilgewater is oil-in-water emulsion emitted from shipboard and it is a source of pollution to the ocean. Wastewater treatment needs fundamental knowledge of the bilgewater surface changes; however, limited information is available. We have reported the first surface characterization of synthetic bilgewater emulsion using time-of-flight secondary ion mass spectrometry (ToF-SIMS) coupled with optical microscopy. A NAVY synthetic bilgewater emulsion consisting of three oils and a detergent mixture is used as the synthetic bilgewater model. Both fresh and aged emulsion samples are analyzed to determine their droplet size distributions (DSDs) and surface chemical compositions. Our results show that fresh emulsions are largely mono-modal with hydrocarbon fragments as the main surface composition. Aged emulsions are also monomodal with slightly larger size. Both SIMS spectral comparison and Principal Component Analysis (PCA) show that some surfactant components appear on the fresh emulsion surface immediately while larger molecular weight components appear at the aged bilge droplet surface. Our results indicate that the oil-water interface evolves after emulsion droplet forms. More importantly, surface evolution not only changes the bilgewater DSD but also surface chemical composition and reactivity.

bilgewater, droplet size distribution, ToF-SIMS, s↗
DOE OSTI2018

Application of materials informatics to vapor-grown carbon nanofiber/vinyl ester nanocomposites through self-organizing maps and clustering techniques

Data mining and knowledge discovery techniques were employed herein to acquire new information on the viscoelastic, flexural, compressive, and tensile properties of vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites. Formulation and processing factors (curing environment, presence or absence of dispersing agent, mixing method, VGCNF weight fraction, VGCNF type, high-shear mixing time, and sonication time) and testing temperature were utilized as inputs and the true ultimate strength, true yield strength, engineering elastic modulus, engineering ultimate strength, flexural modulus, flexural strength, storage modulus, loss modulus, and tan delta were selected as outputs. The data mining and knowledge discovery algorithms used in this study include self-organizing maps (SOMs) and clustering techniques. SOMs demonstrated that temperature and tan delta had the most significant effects on the output responses followed by the VGCNF high-shear mixing time, and sonication time. SOMs were also used to produce optimal responses using certain combination(s) of inputs. Fuzzy C-means algorithm (FCM) was also applied to discover patterns in the nanocomposite behavior subsequent to a principal component analysis (PCA), which is a dimensionality reduction technique. Utilizing these techniques, the nanocomposite specimens were separated into different clusters based on the testing temperature (30 °C and 120 °C being the most dominant responses), tan delta, high-shear mixing time, and sonication time. Furthermore, the VGCNF/VE specimens were separated into a cluster based on their viscoelastic responses (storage and loss moduli) at the same temperature. The FCM results indicate that, while all nanocomposite properties in the new framework are essential, the viscoelastic responses of the VGCNF/VE specimens are the most significant. Here this work highlights the utility of data mining and knowledge discovery techniques in the context of materials informatics for the discovery of patterns and trends in the material behavior that are not immediately known.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Chemical mixture exposure patterns and obesity among U.S. adults in NHANES 2005–2012

The effect of chemical exposure on obesity has raised great concerns. Real-world chemical exposure always imposes mixture impacts, however their exposure patterns and the corresponding associations with obesity have not been fully evaluated. To discover obesity-related mixed chemical exposure patterns in the general U.S. population. Sparse Decompositional Regression (SDR), a model adapted from sparse representation learning technique, was developed to identify exposure patterns of chemical mixtures with exclusion (non-targeted model) and inclusion (targeted model) of health outcomes. We assessed the relationships between the identified chemical mixture patterns and obesity-related indexes. We also conducted a comprehensive evaluation of this SDR model by comparing to the existing models, including generalized linear regression model (GLM), principal component analysis (PCA), and Bayesian kernel machine regression (BKMR). Eight core exposure patterns were identified using the non-targeted SDR model. Patterns of high levels of MEP, high levels of naphthalene metabolites (ΣOH-Nap), and a pattern of high exposure levels of MCOP, MCNP, and MCPP were positively associated with obesity. Patterns of high levels of BP3, and a pattern of higher mixed levels of MPB, PPB, and MEP were found to have negative associations. Associations were strengthened using the targeted SDR model. In the single chemical analysis by GLM, BP3, MBP, PPB, MCOP, and MCNP showed significant associations with obesity or body indexes. The SDR model exceeded the performance of PCA in pattern identification. Both SDR and BKMR identified a positive contribution of ΣOH-Nap and MCOP, as well as a negative contribution of BP3 and PPB to obesity. Our study identified five core exposure patterns of chemical mixtures significantly associated with obesity using the newly developed SDR model. The SDR model could open a new avenue for assessing health effects of environmental mixture contaminants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

The impact of municipal budgets and land-use management on the hazardous waste production of Malaga municipalities

Economic development and the search for competitiveness have become key issues in regions' economic success. However, despite the direct relationship between economic and environmental management, few land-use plans consider the latter aspect, and city managers delegate the responsibility for environmental impacts to state legislation and private initiatives. This myopic search for competitiveness has meant that a holistic view of environmental issues is often not integrated into municipal decision-making processes. Therefore, this study's objective was to determine the relevant direct and indirect relationships of land management and budgetary procedures of municipalities with overall production levels of hazardous waste. To this end, a primary tourist region, Málaga, was examined in terms of how this waste's environmental impacts can affect the region's vital tourism sector. This research used principal component analysis, regression by ordinary least squares, cluster analysis in two stages and a means test to compare the data for the Province of Malaga's subregions. The results confirm a positive relationship between municipal expenditure and waste production and highlight the environmental benefits of land use involving environmentally non-aggressive crops. The results also reveal a negative relationship between waste production and financial assets and a direct relationship between unproductive land and the production of hazardous waste. The findings also highlight the necessity of raising awareness about the need for collaboration between different agents, especially in the development of inter-municipal strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC -heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2%, recall of 89.7%, precision of 88.5% and F1-score of 89.2%. Finally, these results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Data analytics for leak detection in a subcritical boiler

For decades, boiler leaks have been the leading cause of forced outages in the coal-fired unit. The leak occurrences are currently escalating since the existing plants must satisfy faster-ramping rates to support grid operation. Data analytics including Principal Component Analysis, Canonical Variate, and Fisher Discriminant Analysis were combined for detecting and characterizing the leak in a commercial 650 MW subcritical coal-fired power plant. The combined approach was shown to be highly effective in the fault investigation that would not have been easily achieved by an individual technique. The variability in both training and validation datasets was first evaluated using PCA. Then, the CV-FDA was employed to discriminate among faults, and to categorize the processed data into two main groups: no-leak (0) and leak (1), providing the timeframe and location of the leak occurrence. Furthermore, about 8,014 observations from 81 process variables were initially included in the calculation, while the variable count was reduced to 4 with less than 1% misclassification rate in total observations. Finally, the leak was isolated in the waterwall section. Thus, the outcome of this research may provide early detection and isolation of faulty operations in the coal-fired power plant that involves a considerable number of process variables.

20 FOSSIL-FUELED POWER PLANTS↗