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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 91 records · Page 5

Salience Assignment for Multiple-Instance Regression

We present a Multiple-Instance Learning (MIL) algorithm for determining the salience of each item in each bag with respect to the bag's real-valued label. We use an alternating-projections constrained optimization approach to simultaneously learn a regression model and estimate all salience values. We evaluate this algorithm on a significant real-world problem, crop yield modeling, and demonstrate that it provides more extensive, intuitive, and stable salience models than Primary-Instance Regression, which selects a single relevant item from each bag.

regression↗

Geometrical defect detection for additive manufacturing with machine learning models

This study proposed a scheme based on Machine Learning (ML) models to detect geometric defects of additively manufactured objects. The ML models are trained with synthetic 3D point clouds with defects and then applied to detect defects in actual production. Using synthetic 3D point clouds rather than experimental data could save a huge amount of training time and costs associated with many prints for each design. Besides distance differences of individual points between source and target point clouds, this scheme uses a new concept called “patch” to capture macro-level information about nearby points for ML training and implementation. Numerical comparisons of prediction results on experimental data with different shapes showed that the proposed scheme outperformed the existing Z-difference method in the literature. Five ML methods (Bagging of Trees, Gradient Boosting, Random Forest, K-nearest Neighbors and Linear Supported Vector Machine) were compared under various conditions, such as different point cloud densities and defect sizes. Bagging and Random Forest were found the two best models regarding predictability; and the right patch size was found to be at 20. The proposed ML-based scheme is applicable to in-situ defect detection during additive manufacturing with the aid of a proper 3D data acquisition system.

Additive manufacturing↗

Combined Experimental and Modeling Study of the Interactions of Acid Gas with Common Spacecraft Surfaces for Fire Safety Applications

A fire in a spacecraft poses detrimental consequences and risks mission success in addition to crew safety. This is compounded during long-duration missions when the crew has limited options to recover from a fire. A common spacecraft fire concern is the smoldering of wire insulation, typically made from Polyvinyl chloride (PVC) or Polytetrafluoroethylene (PTFE). This creates acid gases such as Hydrogen Chloride (HCl), Hydrogen Fluoride (HF) and Hydrogen Cyanide (HCN). These poisonous gases are hazardous to the crew. They also interact with common surfaces within the spacecraft more than dominant combustion products such as CO2 and H2O. This makes them more difficult to track for potential fire detection techniques, or for postfire clean-up. It is imperative to be able to understand and predict the fate of these poisonous species in a microgravity environment in order to design a safe vehicle. HCl interacts with a number of materials inside a spacecraft. Primary among these materials is aluminum, which is abundantly used due to its strong and light weight nature. Aluminum has a natural oxide layer that protects it from corrosion but is typically treated to enhance this oxide layer. Among these treatments is a chromate conversion coating (CCC), which provides a thin enough protective oxide layer to still conduct electricity, and a traditional anodized material that has a thicker oxide layer that does not conduct electricity. Nomex is another common material found inside a spacecraft. It is a flame-resistant woven polymer that is related to nylon. This commercially available material is used for cargo storage bags and as a fire barrier. Physics-based models were developed to predict the uptake of HCl by these materials. The ultimate objective of these models is to predict the fate of HCl within the spacecraft so that sensors can be placed in meaningful locations in future missions based on the model predictions. To support these modeling efforts, experiments were performed in a cast acrylic test cell that measured the difference between the inlet and outlet concentration of HCl after inserting a sample rod of the test material. Different uptake capacities were realized for each type of sample tested. A computational fluid dynamics model (CFD) model of the reactor was then constructed that used a one-step global reaction rate with calibratable reaction (or kinetic) constants. These constants were calibrated to match the HCl uptake on the CCC aluminum samples, and the same kinetic constants were then tested for the stock and anodized aluminum samples. Model predictions matched the experimental data for the stock aluminum, and to a much lesser extent, the anodized aluminum. The model was additionally validated at different flow rates, sample surface areas, and inlet concentrations, and showed good agreement for all stock and CCC samples. The model did not accurately predict the HCl uptake in the anodized samples compared to the other two types of aluminum. Adjusting the kinetic constants and transport properties did little to improve the prediction. X-Ray Photoelectron Spectroscopy (XPS) was used to determine that the oxide layer thickness of anodized aluminum is approximately 5,000 nm, compared to 250 nm for CCC and 50 nm for stock. XPS also revealed presence of chlorine further down in the aluminum oxide layer in anodized samples than CCC and stock samples after the samples were saturated with HCl, indicating that accounting for diffusion of HCl into the oxide layer is important for accurate prediction of HCl uptake onto anodized aluminum. Consequently, a multi-scale model was developed and tested. First, a single pore inside the anodized aluminum oxide layer was modeled and is referred to as the pore-scale model. In this model, HCl diffused through the pore and reacted with the aluminum oxide pore wall to create aluminum chloride. The sample was then saturated when the mass transfer resistance through the growing aluminum chloride layer became too large for the HCl to reach the aluminum oxide wall and continue the reaction. This pore-scale model was coupled to the reactor-scale model using a concentration-dependent diffusion coefficient, resulting in much more accurate predictions (approximately half the sum square error of the aforementioned reactor-scale model that produced good agreement for stock and CCC) for a variety of operating conditions. The amount of water vapor or relative humidity (RH) in the flow during a reactor experiment was determined to influence HCl uptake. Experiments were performed to understand the interaction of gaseous HCl with aluminum surfaces in the presence of water vapor. The results show that increasing levels of RH increased the capacity of aluminum to adsorb HCl but decreased the capacity of Nomex to uptake HCl. A series of tests were performed on individual aluminum samples after they had been saturated with a fixed concentration of HCl in dry air conditions with the goal of determining how their HCl uptake capacity changes after various treatments with water relative to the original saturation tests. HCl-saturated aluminum samples subjected to a second dry air flow at the same HCl concentration as the original test had an uptake of 23.5% of the original sample with no treatment in between. Saturated aluminum samples subjected to an air flow with a RH of 90% in between tests had an uptake of 35.6% of the original. Saturated aluminum samples submerged in distilled water for 12 hours in between tests had an uptake of 82.2% of the original sample. Previously saturated aluminum tested with HCl and a 50% RH air flow resulted in similar uptake characteristics in multiple repeated tests. The experiments show the profound effect water vapor has on HCl uptake onto aluminum surfaces. In the samples subjected to water vapor or liquid water, capillary condensation and capillary diffusion alters the transport of HCl significantly. A model was proposed that developed a relationship between RH and the coefficient of HCl diffusion in aluminum chloride. This produced an “S-shaped” curve with diffusion coefficient as a function of RH, with 45% RH represented as the point where the diffusion coefficient is halfway between no water saturation and 100% water saturation in the aluminum chloride product layer. No difference in uptake characteristics for the experiment or model were realized between 50% and 62% RH. The results from the large-scale microgravity experiment, Saffire, are discussed as they pertain to the fate of HCl throughout a spacecraft. HCl was released, both as a standalone event, and in concurrence with the burning of a structured cloth. These events only produced a small response in the far field HCl sensor, while a PMMA burn that did not produce HCl had a significantly greater response. A ground-based large-scale facility was constructed to flow acid gas at the scale and configuration realized in the Saffire experiments. A CFD model of this duct was constructed to test kinetic parameters developed in this work at a larger scale and different geometric configuration and to predict the results of the large-scale facility. The models developed in this work were used to interpret the results of the microgravity tests and lead the discussion on what further experiments and models are needed in order to predict the fate of acid gas in a spacecraft environment. To summarize, the major contributions of this work are as follows: the capacity to uptake HCl, with and without the presence of water vapor, was measured for a variety of real spacecraft surfaces. Several different models (single reactor-scale, multiscale, spacecraft-scale) were developed and with the aid of modeling, the rate of uptake for those surfaces was also predicted and validated. The kinetic parameters determined from the small-scale reactor experiments and models were used to predict large-scale and microgravity tests. Conclusions from this research will be used in the design of spacecraft vehicles and large-scale microgravity fire safety experiments. The models built by this work will aid designers in sensor placement and could be used to predict acid gas transport from fires in partial gravity, as would be seen in Lunar and Martian habitats.

fire safety↗

QED: A Powerful Query Equivalence Decider for SQL

Checking query equivalence is of great significance in database systems. Prior work in automated query equivalence checking sets the first steps in formally modeling and reasoning about query optimization rules, but only supports a limited number of query features. In this paper, we present Qed, a new framework for query equivalence checking based on bag semantics. Qed uses a new formalism called Q-expressions that models queries using different normal forms for efficient equivalence checking, and models features such as integrity constraints and NULLs in a principled way unlike prior work. Our formalism also allows us to define a new query fragment that encompasses many real-world queries with a complete equivalence checking algorithm, assuming a complete first-order theory solver. Empirically, Qed can verify 299 out of 444 query pairs extracted from the Calcite framework and 979 out of 1287 query pairs extracted from CockroachDB, which is more than 2× the number of cases proven by prior state-of-the-art solver.

Computer Science↗

Advances in modeling aerodynamic decelerator dynamics.

The Viking entry vehicle uses a lines-first type of deployment in which the parachute, packed in a deployment bag, gets ejected rearward from the vehicle by a mortar. As the bag moves rearward, first the lines are unfurled and then the canopy. An analysis of the unfurling process is conducted, giving attention to longitudinal and rotational dynamics. It is shown that analytical modeling of aerodynamic systems provides significant information for a better understanding of the physics of the deployment process.

Whitlock, C. H.↗

Multimodal Data Representation with Deep Learning for Extracting Cancer Characteristics from Clinical Text

This paper presents a multimodal data representation to improve the performance of deep learning models for extracting cancer key characteristics from unstructured text in pathology reports. Specifically, in addition to using the text as the input to deep learning models, we use concept unique identifiers (CUIs) as another source of information to the models. We analyze the performance of different text and CUI data representations, including word embeddings and bag of embeddings (BOE), with a convolutional neural network (CNN) and a fully connected multilayer perceptron neural network (MLP-NN). The high level document embeddings from text and CUI inputs are combined by concatenating them and then applying a classifier. The model is used for extracting cancer subsite and histology from pathology reports. These two classification tasks have a large number of labels, i.e. 317 for subsite and 556 for histology, with extreme class imbalance. We compare the performance of the developed DL models across the two tasks based on micro- and macro-F1 scores. The evaluation shows that a multi-channel DL model that utilizes text represented by word embeddings and CUIs represented by BOE outperforms other DL models. Also, this approach significantly improves the model performance on low prevalence classes.

Alawad, Mohammed↗

Reduction Mechanism of Iron Oxide Briquettes by Carbonaceous Materials Extracted from Blast Furnace Dust

The reducibility of carbonaceous powder extracted from blast furnace dust was investigated. In this study, pickling treatment was used for BF bag dust and BF gravitational dust to extract the carbonaceous material. The structural characteristics of carbonaceous material were studied by X-ray diffraction, scanning electron microscope, and energy-dispersive spectrometer. The reduction of ferric oxide and carbon gasification experiments were researched by thermogravimetry. The results show that the pickled raw coal gives the highest reducibility, which is followed by BF bag dust and BF gravitational dust. The reason for this is that the higher disordered crystalline structure leads to stronger gasification reactivity, and further results in stronger reduction reactivity. Based on the randomized nucleation model, the kinetic analysis shows that the activation energies for the reduction reaction of BF gravitational dust, BF bag dust, and raw coal after pickling are 300.14, 180.43, and 124.62 kJ mol{sup −1}, respectively.

36 MATERIALS SCIENCE↗

Human Automation Teaming: Lessons Learned and Future Directions

Full autonomy seems to be the goal for system developers in almost every area of the economy. However, as we move from automated systems to autonomous systems, designers have needed to insert humans to oversee automation that has traditionally been brittle or incomplete. This creates its own problems as the operator is usually out of the loop when the automation hands over problems that it cannot handle. To better handle these situations, it has been proposed that we develop human automation teams that have shared goals and objectives to support task performance. This paper will describe an initial model of Human Automation Teaming (HAT) which has three elements: transparency, bi-directional communications, and human-directed execution. Transparency in our model is a method for giving insight into the reasoning behind automated recommendations and actions, bi-directional communication allows the operator to communicate directly with the automation, and finally the automation defers execution to the human. The model was implemented through a number of features on an electronic flight bag (EFB) which are described in the paper. The EFB was installed in a mid-fidelity flight simulator and used by 12 airline pilots to support diversion decisions during off-nominal flight scenarios. Pilots reported that working with the HAT automation made diversion decisions easier and reduced their workload. They also reported that the information provided about diversion airports was similar to what they would receive from ground dispatch, thus making coordination with dispatch easier and less time consuming. These HAT features engender more trust in the automation when appropriate, and less when not, allowing improved supervision of automated functions by flight crews.

Human-Autonomy Teaming↗

Developing Physiologic Models for Emergency Medical Procedures Under Microgravity

Several technological enhancements have been made to METI's commercial Emergency Care Simulator (ECS) with regard to how microgravity affects human physiology. The ECS uses both a software-only lung simulation, and an integrated mannequin lung that uses a physical lung bag for creating chest excursions, and a digital simulation of lung mechanics and gas exchange. METI's patient simulators incorporate models of human physiology that simulate lung and chest wall mechanics, as well as pulmonary gas exchange. Microgravity affects how O2 and CO2 are exchanged in the lungs. Procedures were also developed to take into affect the Glasgow Coma Scale for determining levels of consciousness by varying the ECS eye-blinking function to partially indicate the level of consciousness of the patient. In addition, the ECS was modified to provide various levels of pulses from weak and thready to hyper-dynamic to assist in assessing patient conditions from the femoral, carotid, brachial, and pedal pulse locations.

Parker, Nigel↗

Developing Physiologic Models for Emergency Medical Procedures Under Microgravity

Several technological enhancements have been made to METI's commercial Emergency Care Simulator (ECS) with regard to how microgravity affects human physiology. The ECS uses both a software-only lung simulation, and an integrated mannequin lung that uses a physical lung bag for creating chest excursions, and a digital simulation of lung mechanics and gas exchange. METI s patient simulators incorporate models of human physiology that simulate lung and chest wall mechanics, as well as pulmonary gas exchange. Microgravity affects how O2 and CO2 are exchanged in the lungs. Procedures were also developed to take into affect the Glasgow Coma Scale for determining levels of consciousness by varying the ECS eye-blinking function to partially indicate the level of consciousness of the patient. In addition, the ECS was modified to provide various levels of pulses from weak and thready to hyper-dynamic to assist in assessing patient conditions from the femoral, carotid, brachial, and pedal pulse locations.

Parker, Nigel↗

Continuous integration data-driven platform of industrial-scale subsurface storage for real-time analytics

This project helped address the growing need for efficient and scalable models to support geological carbon and energy storage, which are crucial for achieving net-zero emissions. Traditionally accurate high-fidelity numerical models have been used to simulate relevant storage processes under a handful of processes, however such models are computationally demanding, making uncertainty quantification impractical. Consequently, we first developed a machine learning framework, based on Graph Neural Operators (GNOs), to improving the accuracy of model predictions for a fixed computational budget. We then developed an Ensemble of Improved Neural Operators (ENO), which uses bagging and Monte Carlo dropout techniques, to further improve prediction accuracy. Lastly, we developed the way to explain progressive transfer learning methods to reduce the amount of training data and computational cost of training (i.e., reduce trainable parameters) when using our models for multiple storage sites. Our numerical investigation, which used real-world case studies, demonstrated that our framework can significantly improve the safety and efficiency of geological storage operations, with potential applications in other domains such as geothermal reservoirs and climate modeling.

54 ENVIRONMENTAL SCIENCES↗

Modeling Uprighting of the Orion Crew Module using a Finite Element Method with Wave Coupling and Structural Deformability

The Crew Module Uprighting System (CMUS) for the Orion Crew Module (CM) includes five bags that inflate after splashdown to rotate and upright the CM as necessary. Wave conditions can be severe enough to damage the bags and tethers, which could make the recovery operation hazardous. The CMUS therefore has design limits that ultimately constrain where the CM can land based upon sea conditions. This manuscript describes a full fluid-structure interaction computational model being used for the CMUS. It is a finite element model that uses explicit time integration to directly simulate uprighting, a computational approach usually reserved for short duration, highly transient phenomena and novelly applied here. The goal of this current effort is to determine the feasibility of this approach for CMUS uprighting, and to estimate the loads the CMUS bags experience in different wave conditions. The results from this work will be used to supplement other experimental and computational data in support of NASA’s Artemis program.

John Puryear↗

Exploring natural aerosol formation from DMS oxidation and implications for aerosol forcing (Final Technical Report)

Because of their interactions with sunlight and with clouds, aerosol particles in the atmosphere can play an important role in the Earth’s climate. Understanding this role, and climate change in general, requires understanding the sources of these aerosol particles – not only anthropogenic sources (such as particles emitted from fossil fuel combustion) but also natural sources as well. One of the most important natural aerosol sources is the atmospheric oxidation of dimethyl sulfide (DMS). DMS, a gas emitted by oceanic phytoplankton, is the largest natural source of sulfur to the atmosphere. Once in the air it undergoes natural oxidation processes, converting the sulfur into sulfate, a major component of atmospheric aerosol. However, the chemical mechanism by which DMS oxidation forms sulfate particles is highly complex and uncertain. The goal of this project was therefore to better understand the chemistry of DMS oxidation, via laboratory and global-modeling studies. In the laboratory studies, DMS was oxidized in an environmental chamber (a large Teflon bag surrounded by UV lights to simulate sunlight), under a range of atmospheric conditions. A number of instruments were used to measure the reaction products (gases and particles) within the chamber. The total sulfur in the products equaled the sulfur in the reacted DMS, indicating that all major sulfur-containing species were measured. Yields of these products, and rates of important processes within the mechanism, were determined, providing improved parameters for use in chemical mechanisms. On the global modeling side, a detailed DMS oxidation scheme (that included chemistry occurring in both the gas phase and in cloud droplets) was incorporated into the Community Earth System Model (CESM), and the effects of this more complex mechanism on predicted sulfate levels and climate effects were explored. The expanded chemistry scheme was found to increase global sulfate levels under present-day conditions (~9%) and even more substantially (~30%) under preindustrial conditions. This additional predicted sulfate has implications for our understanding of the role of present-day aerosol pollution on climate, though because of in-cloud chemistry this effect is quite complex, highlighting the need for additional study.

54 ENVIRONMENTAL SCIENCES↗

Model Validation for Bigelow Expandable Activity Module (BEAM) with Stowage

The Bigelow Expandable Activity Module (BEAM) was a collaboration between NASA and Bigelow Aerospace to develop an expandable habitat technology that could be used for future space exploration missions such as the Artemis program. On April 26, 2016, the BEAM was launched and berthed to the International Space Station (ISS) on the aft port of Node 3. After the originally planned two-year mission life, BEAM was approved for use as a stowage module for a 109-cargotransfer bag equivalent (CTBE) to alleviate stowage limitations onboard the ISS. The present work builds on the previous 2017 model validation to flight data that demonstrated that BEAM could generally accommodate cargo stowage without adversely affecting BEAM thermal control, primarily for condensation management. Once the stowage was configured in early 2019, model validation efforts resumed. The thermal model revisions included the CTBE stowage and updated CFD-derived heat transfer coefficients for various intra-module ventilation (IMV) flow rates. The coefficients were provided by the Crew and Thermal Systems Division (EC) at NASA JSC and Jacobs Technology. The model is now revalidated and able to predict minimum temperatures with good agreement to flight temperatures (within 1 °C) for both historic and recurring minimum values. Additionally, the model is partially validated for reduced flow rates. With this model, the team was able to provide new flight rule recommendations for condensation and IMV operations management.

Sydney Taylor↗

Relativistic strange stars in Tolman–Kuchowicz spacetime

Highlights: • We study relativistic strange star using Tolman–Kuchowicz metric. • Bag constant (55–75) MeV/fm{sup 3} remains within the predicted range for stable strange star. • Using observed values for mass and radius, we calculate different physical parameters. • This model satisfies energy conditions, TOV equations and all other stability criteria. • Solutions are completely free from any singularity and give stable stellar system. - Abstract: In this article we propose a relativistic model of a static spherically symmetric anisotropic strange star with the help of Tolman–Kuchowicz (TK) metric potentials (Tolman, 1939 and Kuchowicz, 1968). The form of the potentials are λ(r)=ln(1+ar{sup 2}+br{sup 4}) and ν(r)=Br{sup 2}+2lnC where a, b, B and C are constants which we have to evaluate using boundary conditions. We also consider the simplest form of the phenomenological MIT bag equation of state (EOS) to represent the strange quark matter (SQM) distribution inside the stellar system. Here, the radial pressure p{sub r} relates with the density profile ρ as follows, p{sub r}(r)=(1/3)[ρ(r)−4B{sub g}], where B{sub g} is the Bag constant. To check the physical acceptability and stability of the stellar system based on the obtained solutions, we have performed various physical tests. It is shown that the model satisfies all the stability criteria, including nonsingular nature of the density and pressure, implies stable nature. Here, the Bag constant for different strange star candidates are found to be (68–70) MeV/fm{sup 3} which satisfies all the acceptability criteria and remains in the experimental range.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Spatial Mapping of Riverbed Grain-Size Distribution Using Machine Learning

Recent alluvial sediments in riverbeds play a significant role in controlling hydrologic exchange flows (HEFs) in river systems. The alluvial layer is usually associated with strong heterogeneity in physical properties (e.g., permeability and hydraulic conductivity), which affects local HEFs and therefore biogeochemical processes. The spatial distribution of these physical properties needs to be determined to inform the numerical models used to reveal the realistic hydro-biogeochemical behaviors. Such information can be obtained based on the intrinsic link between sediment grain-size distribution and hydraulic properties where sediment texture information is available. However, grain-size measurements are usually spatially sparse and do not have adequate coverage and resolution, particularly for a relatively large domain such as the Hanford Reach of the Columbia River. In this paper, we adopted machine learning (ML) approaches for categorizing and mapping the spatial distributions of riverbed substrate grain size and filling in missing areas of substrate data using the ML models along the reach. Such ML models for substrate size mapping were trained at 13,372 locations using measured substrate sizes along with observed and simulated attributes, including bathymetric attributes (e.g., elevation, slope, and aspect ratio) from LIDAR and bathymetric surveys, and hydrodynamic properties (e.g., water depth, velocity, shear stress, and their statistical moments). An ensemble bagging-based ML technique, Random Forest, was adopted to identify the most influential factors as predictors to develop the predictive models with over-fitting issues addressed. The models were evaluated with respect to each individual substrate size class and the lumped group, and then used to generate the final substrate size maps covering all the grid cells in the numerical modeling domain.

54 ENVIRONMENTAL SCIENCES↗

Compartment Fire Modeling of a Crew Cabin in 1-g and Lunar-g

A fire inside a spacecraft poses one of the greatest dangers to the crew and mission success. As missions become more complex and longer in duration, the risk of a fire becomes more probable and catastrophic. A greater understanding of the effects of a fire inside a crewed vehicle at potential exploration atmospheres is needed. These exploration atmospheres, such as those being proposed for upcoming Lunar missions, include higher oxygen concentrations and lower pressures, also known as Normoxic conditions. Full scale fire testing, such as those performed during previous space programs, is the most straightforward way to obtain this understanding. These tests are difficult to implement in 1-g and even more challenging to attempt in Lunar-g. Modeling can help inform experiments aimed at determining flammability properties of common materials at exploration atmospheres, as well as determine the effect a fire has inside a spacecraft. A model can also be easily simulated in Lunar-g to predict the effect of gravity on fire propagation. This work focuses on a model of a theoretical partial crew cabin, with potential fire sources being a laptop and a Nomex sleeping bag. A simulation of the crew cabin during a high Heat Release Rate (HRR) fire and low HRR fire at 1-g was compared to simulations at Lunar-g. It was found that in both the low and high HRR cases, the temperature in the cabin was higher in the Lunar-g case than in the 1-g case. This is likely due to the hot products moving out the door of the crew cabin faster in the 1-g case. Modeling case studies like these will help guide future full-scale flammability experiments on Earth and allow for the prediction of fire spread in a Lunar gravity environment in order to design a safer crew cabin.

Fire Safety↗

Fouling modeling and prediction approach for heat exchangers using deep learning

In this article, we develop a generalized and scalable statistical model for accurate prediction of fouling resistance using commonly measured parameters of industrial heat exchangers. This prediction model is based on deep learning where a scalable algorithmic architecture learns non-linear functional relationships between a set of target and predictor variables from large number of training samples. Here, the efficacy of this modeling approach is demonstrated for predicting fouling in an analytically modeled cross-flow heat exchanger, designed for waste heat recovery from flue-gas using room temperature water. The performance results of the trained models demonstrate that the mean absolute prediction errors are under 10 –4 KW –1 for flue-gas side, water side and overall fouling resistances. The coefficients of determination (R 2 ), which characterize the goodness of fit between the predictions and observed data, are over 99%. Even under varying levels of measurement noise in the inputs, we demonstrate that predictions over an ensemble of multiple neural networks achieves better accuracy and robustness to noise. We find that the proposed deep-learning fouling prediction framework learns to follow heat exchanger flow and heat transfer physics, which we confirm using locally interpretable model agnostic explanations around randomly selected operating points. Overall, we provide a robust algorithmic framework for fouling prediction that can be generalized and scaled to various types of industrial heat exchangers.

42 ENGINEERING↗