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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Post-Deployment Characterization of Glass Fiber-Reinforced Thermoset and Thermoplastic Composite Tidal Turbine Blades: Preprint

In 2021, the National Renewable Energy Laboratory (NREL) supported Verdant Power with the most successful tidal energy deployment in U.S. history. Three of their tidal turbines were deployed as part of the Roosevelt Island Tidal Energy project. Initially, the three rotors were manufactured from glass fiber-reinforced epoxy composites. Midway through the deployment, one rotor was replaced with one manufactured at NREL. Instead, it was infused with a novel, infusible thermoplastic resin system. Since the deployment, one epoxy rotor and one thermoplastic rotor were returned to NREL for continued materials and manufacturing research. The two rotors underwent full-scale structural testing before being sectioned and cut into specimens for a variety of manufacturing quality tests, thermos-mechanical characterization, and evaluation of material performance in marine environments, to understand the key differences between the fiberglass reinforced epoxy and Elium composites used for the respective rotors. Matrix burnoff tests showed that the Elium blades had a considerably higher fiber volume fraction compared to the epoxy blades (61% vs. 49%). Environmental aging of the specimens showed that the epoxy laminates absorbed more water over the conditioning period, however, it was determined that the Elium laminates had higher diffusion coefficients, so initially absorbed water faster. Finally, one full epoxy blade and one full Elium blade were conditioned at ambient temperatures for up to 11 months, while taking periodic mass measurements. The datasets were extrapolated out to assume a full 20 year operational life span and it was determined that the blades would not reach full saturation during that time span.

composite manufacturing↗
DOE OSTI2024

All-digital Sensor System for Distributed Downhole Pressure Monitoring in Unconventional Fields

This project developed and validated (through field tests) a new low-cost all-digital pressure sensing technology for in situ distributed downhole pressure monitoring in unconventional oil and gas (UOG) fields. The all-digital sensing technology uses a built-in non-electric analog-to-digital converter (ADC) to transform the pressure information into a combination of binary (ON/OFF) states. As such, the system does not need downhole electronics for signal conditioning and telemetry. The all-digital sensors can be remotely logged over a long distance, and many sensors can be multiplexed for distributed sensing. Based on a review of unconventional wells in the Lower 48 states, the specification of the sensor is to measure pressure up to 69 MPa (10,000 psi) and temperature up to 250°C. A sensor with a helical bourdon sensing element and a digital signal decoder of 50 mm diameter and 109 mm length was constructed. The helical bourdon sensing element was made of 304L stainless steel and filled with motor oil. The digital converter was made up of 8 digital reading pads constructed of high-temperature epoxy with conductive inserts made of stainless steel. The sensor had a linear response to pressure with an accuracy of 0.14 MPa (20 psi). To withstand the high pressure, the sensor was enclosed in a stainless-steel pressure housing with a wall thickness of 5.5 mm, a diameter of 73 mm, and a length of 724 mm. In the laboratory tests, the sensor exhibited no temperature-related effects on the results. The sensor did not show drift over a 14-day test period at elevated pressure. A field test was conducted where the sensor was deployed in a test wellbore at the Quest drilling test facility to a depth of 0 feet over three weeks. The sensor was attached to the production rods, along with a downhole reference sensor of PPS27 type, which is a permanent downhole monitoring system. During the testing phase, the test well annular blow-out preventer was closed, and the well was pressurized at the surface to 11 MPa (1600 psi). The sensor read the elevated bottom hole pressure of 1500 psi. A multiplexing unit was created for the sensor to deploy multiple sensors on one data transmission line in a distributed approach. The multiplexing unit was tested in a simulated environment of 3048 m (10,000 ft) with five sensors distributed. The sensors were pressurized at different intervals. The multiplexed sensors recorded the correct pressure, and the multiplexing did not interfere with the readings. The proposed concept of an all-digital pressure sensor for harsh downhole environments was designed, manufactured, and tested in the laboratory and tested at the field to a up to 69 MPa and 250°C. This technology has high-temperature tolerance and has potential in downhole areas outside oil and gas, such as carbon capture and storage (CCS) and geothermal wells. The sensor concept has been proven in this project, but to create a commercially viable product, manufacturing a sensor with a smaller diameter needs to be performed.

02 PETROLEUM↗
DOE OSTI2024

The Effect of Luminance Pattern on Nighttime Discomfort Glare Response - CRADA 653 (Abstract)

Light Emitting Diode (LED) adoption is critical for widespread energy savings from commercial outdoor lighting systems. A complaint from the public, concerns glare from LED light fixtures especially those with exposed LED arrays. This human factor study will examine luminance uniformity of the fixture aperture to identify parameters related to this response. The outcome will inform optical design by lighting manufacturers, retaining LED energy efficiency while mitigating glare. The results may also lead to improved industry standard glare metrics for lighting. Pacific Northwest National Laboratory’s (PNNL) Lighting Science and Technology Lab in Portland OR has a purpose-built apparatus with exposed LED arrays that have been used for prior work. It can easily be adapted for use in this experiment. Interchangeable templates will allow changing patterns for the stimulus. Glare ratings from the recruited subjects will be analyzed and reported in a peer-reviewed journal for application by luminaire manufacturers for improved products. The McClung Foundation’s interest is to better understand human perception of lighting, leading to more comfortable and effective visual environments. Members of the Foundation’s Technical Review Committee will be instrumental in reviewing the experimental design, analysis, and the final report. They will also help disseminate information about the results within the lighting community.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Reaction Diffusion Modelling of 3D Pillar Electrodes in Single-Catalyst CO 2 Reduction Cascades

Effective electrochemical CO 2 reduction to liquid fuels requires that the local catalytic environment facilitates the desired reactivity, yet a microscopic understanding of this environment is difficult to achieve from experiment alone. In this work, a 3D reaction-diffusion model was developed to explore the effects of electrode surface area and local geometry on the performance of a heterogeneous catalyst that performs a two-step CO 2 reduction cascade reaction to CO and then CH 3 OH under aqueous conditions. Kinetic parameters for the model were inspired by experimental results using a cobalt phthalocyanine (CoPc) catalyst. Three-dimensional architectures composed of arrays of square pillars with varying dimensions and either smooth or periodically modulated surfaces were tested, revealing the extent to which geometry modulates the performance of the cascade reactions. Although structural variations modulate local concentration gradients, we find that electrochemically active surface area predominantly governs the overall cascade reaction. Moreover, the results suggest that supersaturation of CO, with concentrations up to ten-fold higher than the equilibrium solubility limit, might be critical for more efficient conversion to CH 3 OH. For any given geometry, the spatially averaged ratio of [CO] to [CO 2 ] is dictated by the electrochemically active surface area and determines the yield of CH 3 OH. For a fixed surface area, geometries that spatially confine the electrolyte yield moderate local [CO] to [CO 2 ] ratios within small volumes. In contrast, less confining geometries result in a broader distribution of local ratios spread over larger volumes, with both configurations yielding the same spatially averaged [CO] to [CO 2 ] ratio. These insights provide valuable design principles—highlighting the critical importance of surface area and possibly CO supersaturation—for engineering advanced electrode architectures that leverage intermediate trapping and CO supersaturation to enhance overall performance in tandem CO 2 reduction systems.

COMSOL↗
DOE OSTI2025

Cation Crossover Limits Accessible Current Densities for Zero-Gap Alkaline CO2 Reduction to Ethylene

Traditional CO2 reduction systems often fail in an alkaline environment due to the interaction of CO2 with a high-pH electrolyte, where carbonate and bicarbonate ion formation results in potassium-containing salt precipitation. The presence of the salt crystals causes a reduction in the selectivity of the electrolyzer toward CO2 conversion. Here, the critical operational variables, which elicit the salting out process, are investigated (i.e., ion transport). When the electrolyzer exceeds a critical current density, H2 evolution dominates CO2 reduction due to salt formation, which is confirmed by postmortem cross-sectional SEM-EDS of the electrode. The critical current density decreases with an increasing membrane thickness or anolyte ionic strength. Cathode salt formation is mediated by the unmitigated crossover of cations from the anolyte to the cathode across an anion exchange membrane, through which cations are imperfectly excluded. It is likely that electric field-driven migration promotes an increase in concentration of potassium across the membrane, until, at the critical current density for that electrolyzer arrangement, the concentration of potassium and bicarbonate ions exceeds the solubility limit of KHCO3, leading to salt precipitation.

CO2 reduction↗
DOE OSTI2024

Scalable and compact magnetocaloric heat pump technology

Magnetocaloric heat pumping (MCHP) promises to be more efficient than traditional vapor compression while also eliminating the deleterious effects of gaseous refrigerants. While MCHP devices have shown the temperature spans and efficiencies needed for different heating and cooling applications, they struggle to become commercially viable due to their large size and mass, and resultant high cost. This paper evaluates a baseline MCHP device and explores methods to boost its system power density (SPD). The key components of the baseline system are the gadolinium packed-particle bed active magnetic regenerator (AMR) and a magnetic source composed of permanent magnets and high permeability magnetic steel. To enhance the SPD, the paper evaluates maximizing the AMR volume, opting for first-order magnetocaloric materials, optimizing the magnet and AMR geometry, and reducing the size of magnets and magnetic steel parts. At larger thermal powers, increasing the AMR diameter and the number of magnetic poles were evaluated. Using finite element models, solid models, and estimates of magnetocaloric material performance, thermal powers ranging from 37 W to 44 kW at a nominal 10 K temperature span were projected, and SPD was estimated to improve from 6 W/kg to 81 W/kg. Neglecting end effects, an upper limit of 114 W/g is estimated. Compared to SPD of off-the-shelf compressors with similar environment temperatures, MCHP power density using gadolinium is competitive up to roughly 200 W of cooling power. This is extended to 1 kW when using LaFeSi alloys and up to 3 kW in the limiting case. In conclusion, these results indicate that the performance and mass of MCHP can match that of compressors, which is a critical step toward cost-competitive magnetocaloric technology.

42 ENGINEERING↗
DOE OSTI2026

Perovskite design principles for efficient microwave dry reforming with noble metal free catalysts

Microwave absorbing catalysts have the potential to electrify high-temperature thermal reactions such as the dry reforming of methane process (DRM: CO 2 + CH 4 → 2CO + 2 H 2 ). However, microwave catalysts present unique challenges due to their dual requirements of maintaining microwave absorption in both oxidative and reductive environments and stability across a range of temperatures in inherently non-isothermal reactors. Here, catalyst candidates from the La 0.8 Sr 0.2 CoO 3 -La 0.8 Sr 0.2 NiO 3 -La 0.8 Sr 0.2 MnO 3 perovskite systems were screened (28 total) to identify promising microwave catalysts free of noble metals for dry reforming methane. The best performing candidates met two main criteria. First, they occurred at crystal phase boundaries, giving rise to a pseudocubic perovskite structure. The combined use of Goldschmidt tolerance factor and octahedral tolerance factors appeared to be suitable for predicting pseudocubic perovskites. Second, they provided a balance of reducible metal sites with an irreducible metal oxide support. The best performing catalyst was found to exsolve Ni-Co alloy particles as active sites for the DRM reaction which offered superior resistance to coking for excellent reforming efficiency and stability.

42 ENGINEERING↗
DOE OSTI2025

Coupling Waste Feedstocks to Microbial Protein Production in a Circular Food System

Global food production is a major contributor to greenhouse gas emissions, water consumption, and land use. As an alternative to conventional agriculture, the production of waste-derived microbial protein (MP) holds promise for reducing environmental impacts. MP can be mass-produced in volumetrically scalable fermentation processes on short time scales, enabling facile scale-up with lower greenhouse gas emissions, land use, and water impacts than animal and, in some cases, plant protein. MP can also be produced from waste feedstocks, diverting waste from landfills or the natural environment. This Perspective explores the availability and suitability of waste feedstocks for MP production, suggesting that MP generated from waste feedstocks in the United States could fulfill twice the current national protein demand. Here, we also discuss the biotechnological and separations processes required to produce food-grade MP for human consumption from waste. Key challenges include MP consistency, consumer and regulatory acceptance, and the process utilities (electricity, heat, and nutrients) that account for up to 85% of MP costs and most environmental impacts, all of which present opportunities for innovation in the microbiology and process design spaces. Overall, this work highlights the potential of MP to contribute to a more circular, resilient, and sustainable food system.

09 BIOMASS FUELS↗
DOE OSTI2024

Exploring layer thinning of exfoliated β-tellurene and room temperature photoluminescence with large exciton binding energy revealed in β-TeO 2

Due to its tunable bandgap, anisotropic behavior, and superior thermoelectric properties, device applications using layered tellurene (Te) are becoming more attractive. Here, we report a thinning technique for exfoliated tellurene nanosheets using thermal annealing in an oxygen environment. We characterize different thinning parameters, including temperature and annealing time. Based on our measurements, we show that controlled layer thinning occurs in the narrow temperature range of 325–350 °C. We also show a reliable method to form β-tellurene oxide (β-TeO 2 ), which is an emerging wide bandgap semiconductor with promising electronic and optoelectronic properties. This wide bandgap semiconductor exhibits a broad photoluminescence (PL) spectrum with multiple peaks covering the range of 1.76–2.08 eV. This PL emission, coupled with Raman spectra, is strong evidence of the formation of 2D β-TeO 2 . We discuss the results obtained and the mechanisms of Te thinning and β-TeO 2 formation at different temperature regimes. We also discuss the optical bandgap of β-TeO 2 and show the existence of pronounced excitonic effects evident by the large exciton binding energy in this 2D β-TeO 2 system that reach 1.54–1.62 eV for bulk and monolayer, respectively. Our work can be utilized to have better control over the Te nanosheet thickness. It also sheds light on the formation of well-controlled β-TeO 2 layered semiconductors for electronic and optoelectronic applications.

42 ENGINEERING↗
DOE OSTI2024

Pressurized-Water Reactor Core Design using Multi-Objective Plant Fuel Reload Optimization Platform

The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program Risk-Informed Systems Analysis (RISA) Pathway Plant Reload Optimization Project aims to develop an integrated, comprehensive framework offering an all-in-one solution for reload evaluations with a special focus on optimization of core design. The optimization of the fuel loading pattern is one of the most important considerations in reducing the amount of new fuel used in the core. Due to thousands of possible options of core configuration, finding optimal solutions is an unachievable task for a human. The Plant ReLoad Optimization (PRLO) platform which supports artificial-intelligence-based reactor core designing is now fully capable of handling realistic problems. The PRLO Platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses and uses artificial intelligence to support the optimization of core design solutions. The NSGA-II (Non-dominated Sorting Genetic Algorithm-II) optimizer was developed and tested within RAVEN (Risk Analysis and Virtual Environment) to handle many constraints by using an augmented objectives methodology. The demonstration was performed with constrained multi-objective optimization of a 17 × 17 pressurized-water reactor core loading patterns to minimize fuel cost and maximize fuel cycle length.

42 ENGINEERING↗
DOE OSTI2026

Integral Nuclear Data and Benchmarking Needs for Fusion Energy Systems

Fusion energy systems are currently being designed and optimized using radiation transport codes. To deal with the unique environment inside a fusion-based system, many of these designs incorporate novel materials able to withstand the high radiation fields, ensure adequate cooling and thermal protection, and produce tritium. Validation plays a vital role in building trust in the predictive power of these models and computational methods. Validation of a code consists of modeling documented real-world experiments and comparing the code-predicted response to the measured response. Adequate validation requires measured responses from real-world experiments, also known as integral data, that mimic the system being designed, including materials, impinging radiation, and temperature, among other variables. The most trusted integral data are experimental responses that have been through a rigorous benchmarking process that develops a recommended computational model and evaluates all experimental uncertainties. Finally, there are a few research groups around the world that have been producing integral data for fusion applications, but a substantial investment is needed to address the unique validation needs of the fusion community.

Fusion↗
DOE OSTI2026

Predictive Model for Starlink Maritime Performance Using Multi-Horizon RandomForest

Low Earth orbit (LEO) satellite systems have become a crucial enabler of broadband access for maritime industries, where traditional networks are unavailable. However, the high mobility of LEO constellations and constantly changing weather conditions result in unpredictable link fluctuations, limiting the ability of maritime platforms to plan bandwidth usage proactively. To the best of our knowledge, no prior work has developed a short-term predictive model for maritime LEO connectivity using real experimental field measurements. This paper proposes a data-driven forecasting model that predicts future downlink throughput using multi-horizon RandomForest regression. The model is trained using real experimental coastal measurement data incorporating recent throughput history, network-layer indicators, and environmental variables. The proposed approach reduces mean absolute error by approximately 31% compared to a persistence baseline for 15-minute horizons. It maintains a measurable improvement at 30 minutes, despite increased stochasticity. These findings confirm that proactive bandwidth awareness is feasible on maritime platforms and can effectively support operational decisions such as adaptive streaming, routing, and resource scheduling. The performance gap between forecasting horizons also highlights the need for expanded offshore datasets to improve prediction robustness under harsher maritime environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

RePED 250: A Revolutionary, High Drilling Rate, High-T Geothermal Drilling System and Companion Power Electronics

Geothermal drilling in the United States is drastically slower and more costly than Oil and Gas (O&G) drilling, primarily due to the additional challenges in geothermal wells: hard rock types and high temperatures. The Repetitive Pulsed Electric Drill (250°C) (RePED-250), developed by Tetra Corporation has the potential to change this with a novel electro-crushing technology. RePED-250 releases pulses of electricity through the rock, which causes it to break in tension, instead of compression like conventional rotary drilling. The tensile strength of hard rocks like granite is only ~5% of their compressive strength, requiring less energy, time, and wear on the bit compared to conventional drilling. The RePED drill bit has no moving parts, uses drilling mud to remove rock fragments, and uses standard drill pipe, enabling a direct transition from conventional drilling systems. The objective of this project is to move RePED technology towards commercial readiness for high-temperature geothermal applications. This involves: 1) Creating high-temperature, high power electronic components for the RePED drill capable of operating at the high temperatures of a geothermal well. The target is a 250°C ambient environment. Specifically, capacitors, high voltage switches, and a downhole generator capable of generating the necessary power. 2) Demonstrating RePED’s effectiveness drilling through hard rock (granite) and examining its pathway to commercialization and effect on the US geothermal market. Work on this project has led to significant improvement over the state-of-the-art for all the examined electronics, through design, modeling, and testing of these novel components, though some additional development is still needed to deploy these high-temperature parts in a geothermal well. Besides their use in RePED-250, each component has applications in other industries where the stability of electronics at high-temperature is needed. To address the second barrier to commercialization, a 9” hole was drilled through a sample block of granite, demonstrating the tool’s effectiveness in hard rock. The team investigated RePED-250’s effect on well cost and the geothermal industry, through various techno-economic models described in the report.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Computational insights into hydrogen adsorption energies on medium-entropy oxides

High entropy oxides (HEOs) have emerged as promising catalysts for several important chemical transformations including alkane activation. Hydrogen adsorption energy (HAE) has been used as a key descriptor for many reactions including methane C–H activation and hydrogen evolution reactions. Hence, understanding the relationship between HAEs and the surface chemistry of HEO surfaces could lay the foundation for meaningful correlations among methane C–H activation, HAE, and the complex, local environment of HEO surfaces. Here, we used a medium-entropy oxide as a prototypical system – Mg 0.25 Ni 0.25 Cu 0.25 Zn 0.25 O with a rock-salt structure – to interrogate these relationships. We sampled 2000 different surfaces of its (100) plane and calculated the HAEs at randomly chosen surface O sites using density functional theory (DFT). Our analysis of the 2000 data points reveals that the HAEs at the surface O sites are significantly influenced by the local environment around the adsorption sites, particularly the nature of the metal atom directly below the surface O site where H adsorbs. After comparing several popular graph-neural-network-based machine learning models, we found that the DimeNet++ model performed best achieving satisfactory accuracy in predicting HAEs for both Mg 0.25 Ni 0.25 Cu 0.25 Zn 0.25 O and slightly varied compositions. Our work underscores the promise of such models and the need for further refinement to address the complexity of HEOs.

Song, Haohong [Vanderbilt Univ., Nashville, TN (Un↗
DOE OSTI2025

Model-based Hierarchical Reinforcement Learning for Improved Physical Security Design: A Prototype

Prior work in FY24 developed an adversarial AI agent aid in path analysis of physical protection systems. This agent, trained using a model-based reinforcement learning algorithm, was able to successfully learn the most vulnerable path in facilities. It was able to extend the current state of practice for physical protection design by exhibiting dynamic behavior based on current environmental conditions. Whereas PathTrace largely performs a static, graph-based analysis, the AI agent was able to make decisions based on relative position in the facility, current conditions (was the adversarial agnet discovered?), and proximity to secondary targets. The agent demonstrated some novel capabilities, but had limitations that need to be resolved before it can be used for production purposes. For example, the adversarial agent generalizes poorly and takes a relatively long time to train. Nonetheless, there is still considerable promise for developing the adversarial agent further in order to explore even richer, more dynamic behaviors (e.g., adversary motivations, environmental debris, and more). This work considers a complementary idea; development of a planning agent. The planning agent is envisioned as an auto-complete-like tool that can help accelerate security system design by human experts. The agent would respect existing barriers and sensors placed by a human expert while offering cost-effective suggestions (i.e., implicitly balancing effectiveness with cost) to improve the design. The goal is for this agent to be part of an expert’s toolbox, not to totally upend the current state-of-practice, or to displace human experts. The ultimate goal would be concurrent training of both the adversarial and planning agent together, to learn entirely through self-play. This would represent an entirely new way of performing system deign. We selected a hierarchical, model-based reinforcement learning algorithm to serve as the planning agent. This is an extension of concepts used in the prior FY24 adversarial agent work. There, we had a single agent acting an environment. Here, we have two different sub-agents (policies), working together, to form a complete agent. There is a manager policy, which can select abstract goals on slower time scales, and a worker, which performs primitive actions to reach goals selected by the manager. It is worth noting that this class of algorithm is challenging to work with. From our understanding, our work is one of the first successful uses of model-based reinforcement learning (MBRL) in nuclear energy1 , and likely the first hierarchical model-based reinforcement learning application in nuclear energy. Further, this work is one of the first known attempts to apply AI to perform a design tasks in nuclear energy. Consequently, there were significant implementation challenges and the bulk of the work was focused on successful implementation and algorithm design. The results presented here are very low technology readiness level as a consequence of the lack of related literature, but still represent a significant step forward in the pursuit of applied AI for design.

42 ENGINEERING↗
DOE OSTI2024

TRISO Fuel’s Safety Functions, Contributions to Reactor Safety, and Necessary Safety Limits

Safety functions are the actions, passive or active, that structures, systems, and components of nuclear facility that contribute to the safety of the workers, the public, or the environment. Well-defined safety functions are the foundation of a solid safety case for a reactor. For reactors that use TRISO-coated particles, the TRISO fuel plays an important part of the safety case because of its ability to contain radionuclides in the fuel itself. This ability enables the use of a functional containment strategy for the reactor where radionuclide retention is the primary safety function supported by the safety functions of controlling reactivity control and controlling heat rejection. This paper establishes at a deeper level the role that TRISO fuel plays in each of these safety functions and associated quality assurance and testing requirements for TRISO particle manufacturing to ensure these safety functions. Safety limits necessary to protect these safety functions include: operational limits, time at temperature limits, and fission gas release activity limits. In conclusion, this approach demonstrates the role that specific aspects of TRISO fuel play in protecting the safety of workers, the public, and the environment.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Reactive Transport Modeling with Physics-Informed Machine Learning for Critical Minerals Applications

This study presents a physics-informed neural network (PINN) framework for reactive transport modeling for simulating fast bimolecular reactions in porous media. Accurate characterization of cAhemical interactions and product formation in surface and subsurface environments is essential for advancing critical mineral extraction and related geoscience applications. The proposed methodology sequentially addresses the flow and diffusion–reaction subproblems. The flow field is computed using a mixed formulation, while the diffusion–reaction system is modeled via two uncoupled tensorial diffusion equations reformulated in terms of chemical invariants. PINNs are employed to solve the governing equations, enabling data-efficient, mesh-free prediction of chemical concentration fields. The framework is validated through a series of benchmark problems involving flow in heterogeneous porous media. Initial verification is conducted using patch tests for the flow field, followed by validation of the transport problem with emphasis on preserving non-negativity of concentrations. The complete fast bimolecular reaction scenario is then solved, yielding spatial distributions of reactants and product species. Results demonstrate that the PINNs-based approach effectively captures sharp, mixing-limited reaction fronts and dispersive mixing behavior, offering reliable predictions of reactive plume evolution. These capabilities are crucial for evaluating long-term subsurface behavior in applications such as fluid storage, energy extraction, and efficient extraction of critical minerals.

42 ENGINEERING↗
DOE OSTI2026

Product Defect Detection System: SYSM- 5620 Final Project

Retail sales is a growing market estimated to up to seven percent year over year. With this growing market there is also a trend in growing rate of retail returns, estimated just last year at $\$$850 billion. Retail stores must ensure that products available for purchase remain safe, undamaged, and acceptable to customers throughout their time in the store. This job exists regardless of the specific solution used because stores are always responsible for preventing damaged or defective products from reaching customers and when they fail to this is categorized under operation inefficiencies which accounts for an estimated $\$$12 billion in returns. When defective items remain on the sales floor, stores may experience increased returns, reduced customer satisfaction, loss of customer trust, and potential safety concerns depending on the product type. As a result, the core job to be done is to identify defective products quickly, remove them from the sales floor before they are purchased, and preserve useful information about the defect so that the store can improve its handling, stocking, and supplier coordination over time. The need for a more reliable process is especially important in high volume retail environments where employees manage large numbers of products across many aisles, shelves, and storage areas. In these settings, manual inspection alone can be inconsistent and difficult to sustain at the individual item level. At the same time, broader retail trends continue to emphasize operational efficiency, product visibility, and improved customer experience, creating an opportunity for more automated and data driven defect detection methods.

42 ENGINEERING↗