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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

LAMP Technical Readiness Evaluation Report (Rev. 1)

An internal preliminary evaluation of Critical Technology Elements (CTEs) for the LANSCE Modernization Project (LAMP) was completed. Corresponding Technical Readiness Levels (TRLs) were also determined for all subsystems using the criteria of DOE G 413.3-4A, Technical Readiness Assessment Guide. The scope of the evaluation was limited to the project Work Breakdown Structure (WBS) elements as defined for the RFQ Injector and Drift Tube Linac (DTL) systems only. Integration of Instrumentation and Controls (I&C) and Safety Systems was not considered, although specific technologies as related to the RFQ and DTL systems were included. Other elements of the project such as Shielding, System Design, Technical Management, and additional facility integration needed to enable off-line testing and pre-installation commissioning were also not evaluated. Each technical subsystem element was evaluated for technical readiness, however, not all were found to meet the criteria for a CTE. Only two subsystem elements were determined to meet the CTE criteria. Their associated TRLs are summarized in the table below. These subsystem elements of the project have the lowest technical readiness due to either being new, novel or modified, requiring additional R&D before being capable of meeting the project Key Performance Parameters (KPPs) and subsystem requirements, or present technology exists but has not yet been demonstrated in a relevant environment. All other subsystems were determined to have a TRL of 8, indicating that actual operating systems exist having similar performance requirements as needed for LAMP. Details of the technical readiness evaluation for each subsystem is given in the following sections of this report.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Impact of Vibrational Nonequilibrium on the Supersonic Mode in Hypersonic Boundary Layers

The supersonic mode in hypersonic boundary layers has recently been shown to achieve a larger peak growth rate than the traditional second mode in particular circumstances relevant to free flight. In this environment, high levels of aerodynamic heating are encountered such that incorporation of chemical and vibrational nonequilibrium effects can become critical. The impact of thermal nonequilibrium (TNE) on the supersonic mode in particular has not been thoroughly and systematically investigated. This work uses thermochemical nonequilibrium (TCNE) direct numerical simulation (DNS) and TCNE linear stability theory (LST) to obtain a more complete investigation of the supersonic mode using both nonequilibrium and frozen gas models. The simulation is Mach 5 flow over a 1-mm-nose-radius axisymmetric cone 1 m in length, representative of experimental conditions in ground test facilities. Both LST and DNS results indicate that TNE effects are destabilizing compared with frozen flow and can lead to an N-factor difference of approximately +1 at the end of the cone. LST results also indicate that TNE effects are stabilizing to the second and supersonic modes in comparison with thermal equilibrium. The supersonic mode appeared to be slightly more affected by TNE than the second mode. In conclusion, collectively, the findings in this work suggest that even in ground test facilities where TCNE effects are relatively small compared with free flight, incorporating nonequilibrium models in both the mean flow and stability solvers can have a practical effect on hypersonic boundary-layer transition prediction analyses.

42 ENGINEERING↗
DOE OSTI2022

Inferring safety critical events from vehicle kinematics in naturalistic driving environment: Application of deep learning Algorithms

Advances in sensing technology has enabled the collection of countless terabytes of second-by-second kinematics data. Such data provides opportunities for real-time monitoring of driving behavior and identification of safety critical events (SCEs) including crashes and near crashes. The concept of volatility is relevant in this context, which identifies instability and erratic variations in driving behavior prior to involvement in SCEs. This study utilized vehicle kinematics from a large-scale naturalistic driving data to develop a deep learning approach based on 1D convolutional neural networks (CNN) for inferring SCEs. The data are unique in the sense that such accurate pre-crash data at high fidelity are not available in traditional crash repositories. This study contributes to the literature by providing a first attempt at predicting responses to SCEs by developing deep learning-based CNN architectures using novel driving volatility based kinematic thresholds for a sample of 9553 events. The key contribution lies in developing a volatility-based CNN input layout that is acceptable to CNN schemes and represents the motion kinematics such as speed, acceleration and volatility measures. Several 1D-CNN architectures were developed using layers, numbers of convolutions, layer patterns, and kernels. Shallow and deep architectures were tested, revealing higher accuracy of shallow architectures in detecting SCEs. The optimal number of epochs were identified using an early stopping method while the CNN performance was improved by increasing the number of epochs. The ensemble CNN had the highest predictive accuracy of 95.6% for detection of crashes and near crashes, which was 2.5% higher than the optimal CNN using 20% hold out test data. The ensemble CNN also outperformed classical machine learning models and model performance reported in past studies on detection of SCEs. Finally, these results have implications for identification of safety hotspots and providing real-time alerts and warnings in connected and highly automated vehicle environment including society of automotive engineers levels 3–5.

42 ENGINEERING↗
DOE OSTI2023

Assessment of the Distributed Ledger Technology for Energy Sector Industrial and Operational Applications Using the MITRE ATT&CK® ICS Matrix

In recent times, Distributed Ledger Technology (DLT) has gained significant attention for its potential application in the energy sector. Utilizing blockchain and DLT has demonstrated the ability to enhance the resilience of the electric infrastructure, which will support a more flexible infrastructure and advance grid modernization. However, the deployment of these technologies increases the overall attack surface. The MITRE ATT&CK® matrices have been developed to document an adversary’s tactics and techniques based on real-world observations. The MITRE ATT&CK® matrices provide a common taxonomy for offense and defense and have become a valuable conceptual tool across multiple cybersecurity disciplines for conveying threat intelligence, performing testing through red teaming or adversary emulation, and enhancing network and system defenses against intrusions. The MITRE ATT&CK® for Industrial Control Systems (ICS) matrix was created to provide knowledge about adversary behavior in the ICS technology domain. This study analyzes the relevance of various tactics and techniques across a seven-layer DLT engineering and cybersecurity stack, known as the DLT stack, designed by the Cybersecurity Taskforce under IEEE P2418.5 - Standard for Blockchain in Energy working group sponsored by Power and Energy Systems - Smart Buildings, Loads and Customer Systems (PES/SBLC) Technical Committee. Additionally, this paper identifies specific mitigation strategies tailored to the energy ICS environment

42 ENGINEERING↗
DOE OSTI2023

Near-Cloud Atmospheric Ingredients for Deep Convection Initiation

A lack of routine environmental observations located near deepening cumulus congestus clouds limits verification of important theorized and simulated updraft–environment interaction processes occurring during deep convection initiation (CI). We analyze radiosonde profiles collected during several hundred CI events near a mountain range in central Argentina during the CACTI field campaign. Statistical analyses illustrate environmental conditions supporting radar-observed CI outcomes that span a spectrum of convective cell depths, widths, and durations, as well as events lacking precipitating convection. Tested environmental factors include a large variety of sounding-derived measurements of CAPE, CIN, moisture, terrain-relative winds, vertical shear, and lifted parcel properties, with supplemental model reanalysis of background larger-scale vertical motion. CAPE and CIN metrics do not consistently differentiate CI success from failure. Only a few environmental factors contain consistent monotonic relationships among the spectrum of cloud depths achieved during CI: (i) the depth and strength of background ascent, and (ii) the component of low-level flow oriented parallel to the ridgeline. These metrics suggest that the ability of the surrounding flow to lift parcels to their LFC and terrain-modified flow are consistently relevant processes for CI. Low- to midlevel relative humidity strongly discriminated between CI and non-CI events, likely reflecting entrainment-driven dilution processes. However, we could not confidently conclude that relative humidity similarly discriminated robust from marginal CI events. Circumstantial evidence was found linking cell width, an important cloud property governing the probability of CI, to LCL height, boundary layer depth, depth and magnitude of the CIN layer, and ambient wind shear.

Storm environments↗
DOE OSTI2024

Metal Independent Correlations for Site-specific Binding Energies of Relevant Catalytic Intermediates

Establishing energy correlations among different metals can accelerate the discovery of efficient and cost-effective catalysts for complex reactions. Using a recently introduced coordination-based model, we can predict site specific metal binding energies (∆EM) that can be used as a descriptor for chemical reactions. In this study, we have examined a range of metals including Ag, Au, Co, Cu, Ir, Ni, Os, Pd, Pt, Rh, and Ru and found linear correlations between predicted ∆EM and adsorption energies of CH and O (∆ECH and ∆EO) at various coordination environment for all the considered metals. Interestingly, all the metals correlate with one another under specific surface site coordination, indicating that different metals are interrelated in a particular coordination environment. Furthermore, we have tested and verified for PtPd and PtIr-based alloys that they follow a similar behavior. Moreover, we have expanded the metal space by taking some early transition metals along with few s-block metals and shown a cyclic behavior of the adsorbate binding energy (∆EA) versus ∆EM. Therefore, ∆ECH and ∆EO can be efficiently interpolated between metals, alloys and intermetallics based on information related to one metal only. This simplifies the process of screening new metal catalyst formulations and their reaction energies.

Density function theory↗
DOE OSTI2021

Astrophysical neutrino self-interactions in the high-statistics era

Do neutrinos have sizable self-interactions? They might. Laboratory constraints are weak, so strong effects are possible in astrophysical environments and the early universe. Observations with neutrino telescopes can provide an independent probe of neutrino self (“secret”) interactions, as the sources are distant and the cosmic neutrino background intervenes. We define a roadmap for making decisive progress on testing these interactions. This progress will be enabled by IceCube-Gen2 observations of high-energy astrophysical neutrinos. Critical to this is our comprehensive treatment of the theory, taking into account previously neglected or overly approximated effects, as well as including realistic detection physics. We show that IceCube-Gen2 can realize the full potential of neutrino astronomy for testing neutrino self-interactions, being sensitive to cosmologically relevant interaction models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Process design tools and techno-economic analysis for capacitive deionization

Capacitive deionization (CDI) devices use cyclical electrosorption on porous electrode surfaces to achieve water desalination. Process modeling and design of CDI systems requires accurate treatment of the coupling among input electrical forcing, input flow rates, and system responses including salt removal dynamics, water recovery, energy storage, and dissipation. Techno-economic analyses of CDI further require a method to calculate and compare between a produced commodity (e.g. desalted water) versus capital and operational costs of the system. In this work, we demonstrate a new modeling and analysis tool for CDI developed as an installable Matlab program that allows direct numerical simulation of CDI dynamics and calculation of key performance and cost parameters. The program is provided for free and is used to run open-source Simulink models. The Simulink environment sends information to the program and allows for a drag and drop design space where users can connect CDI cells to relevant periphery blocks such as grid energy, battery, solar panel, waste disposal, and maintenance/labor cost streams. The program allows for simulation of arbitrary current forcing and arbitrary flow rate forcing of one or more CDI cells. We employ validated well-mixed reactor formulations together with a non-linear circuit model formulation that can accommodate a variety of electric double layer sub-models (e.g. for charge efficiency). The program includes a graphical user interface (GUI) to specify CDI plant parameters, specify operating conditions, run individual tests or parameter batch-mode simulations, and plot relevant results. The techno-economic models convert among dimensional streams of species (e.g. feed, desalted water, and brine), energy, and cost and enable a variety of economic estimates including levelized water costs.

42 ENGINEERING↗
DOE OSTI2024

TRUST Sensors in Environments: Fiber Optic Displacement (SE-FOD) Report, Release FY24

The objective of the Delivery Environments (DE) Testbeds to Reduce Uncertainty in Simulations and Tests (TRUST) project is to quantify and help increase confidence in specific areas of computational and experimental capabilities that are applicable to the development, on-target assessment, and qualification of current and future delivery environments. More complete quantification of confidence in experimental and computational capabilities and the sufficient increase of confidence in those capabilities is critical to improving weapons engineering design, qualification, and assessment efforts that are critical to the current and future stockpile. This work uses and provides feedback on analysis tools and experimental results databases for efficient and responsive engineering which are currently under development. Under the TRUST work package, there are five testbeds and their associated engineering analysis baseline models (EABMs): 1. Contact Thermal Conductivity (CTC), 2. Nonlinear Dynamics (ND), 3. Sensors in Environments: Accelerometers (SE-A), 4. Sensors in Environments: Fiber Optic Displacement Gauges (SE-FOD), and 5. Sensors in Environments: Thermocouples (SE-TC). The TRUST project uses single-feature testbeds to quantify uncertainties in specific models and experiments and to identify capability development needs that can help to reduce these uncertainties. Each testbed is designed, configured, and tested in collaboration with groups with design and experimental capability: E-14 and MPA-CINT. The complementary simulations are conducted using W-13 analysis tools and stored in model repositories with plans for incremental progress toward EABM requirements. W-13 extends and exercises the testbed simulations in collaboration with experimentalists for uncertainty quantification of current and future materials, geometries, and environments. Additionally, TRUST is intended to provide engineers in W-13 and E-14 with experience in both numerical simulations and experimental methods through cross-discipline collaborations. Following the introduction to the TRUST project, the remainder of this report focuses on experimental, modeling and simulation, and analysis efforts conducted in FY24 relevant to the TRUST Sensors in Environments: Fiber Optic Displacement (SE-FOD) testbed.

42 ENGINEERING↗
DOE OSTI2024

TRUST Contact Thermal Conductance (TRUST-CTC) Report: FY24

The objective of the Delivery Environments (DE) Testbeds to Reduce Uncertainties in Simulations and Tests (TRUST) project is to quantify and help increase confidence in specific areas of computational and experimental capabilities that are applicable to current and future delivery environments. More complete quantification of confidence in experimental and computational capabilities and the sufficient increase of confidence in those capabilities is critical to improving weapons engineering design, qualification, and assessment efforts that are critical to the current and future stockpile. Staff development will include cross-discipline collaboration to provide engineers with experience in both numerical simulations and experimental methods. This work uses and provides feedback on analysis tools and experimental results databases for efficient and responsive engineering which are currently under development. TRUST currently includes five testbeds and their associated engineering analysis baseline models (EABMs): 1. contact thermal conductivity, (CTC) 2. nonlinear dynamics, (ND) 3. sensors in environments for accelerometers, (SEA) 4. sensors in environments for fiber optic displacement gages, and (SEFOD) 5. sensors in environments for thermocouples (SETC). The TRUST project uses single-feature testbeds to quantify uncertainties in specific models and experiments and to identify capability development needs that can help to reduce these uncertainties. Each testbed is designed, configured, and tested in collaboration with groups with design and experimental capability: E-14 and MPA-CINT. The complementary simulations are conducted using W-13 analysis tools and stored in model repositories with plans for incremental progress toward EABM requirements. W-13 extends and exercises the testbed simulations in collaboration with experimentalists for uncertainty quantification of current and future materials, geometries, and environments. Additionally, TRUST is intended to provide engineers in W-13 and E-14 with experience in both numerical simulations and experimental methods through cross-discipline collaborations. Following the introduction to the TRUST project, the remainder of this report focuses on experimental and analytical efforts conducted in Fiscal Year (FY) 2024 relevant to the TRUST Contact Thermal Conductance (CTC) testbed.

42 ENGINEERING↗
DOE OSTI2024

Differential laboratory passaging of SARS-CoV-2 viral stocks impacts the in vitro assessment of neutralizing antibodies

Viral populations in natural infections can have a high degree of sequence diversity, which can directly impact immune escape. However, antibody potency is often tested in vitro with a relatively clonal viral populations, such as laboratory virus or pseudotyped virus stocks, which may not accurately represent the genetic diversity of circulating viral genotypes. This can affect the validity of viral phenotype assays, such as antibody neutralization assays. To address this issue, we tested whether recombinant virus carrying SARS-CoV-2 spike (VSV-SARS-CoV-2-S) stocks could be made more genetically diverse by passage, and if a stock passaged under selective pressure was more capable of escaping monoclonal antibody (mAb) neutralization than unpassaged stock or than viral stock passaged without selective pressures. We passaged VSV-SARS-CoV-2-S four times concurrently in three cell lines and then six times with or without polyclonal antiserum selection pressure. All three of the monoclonal antibodies tested neutralized the viral population present in the unpassaged stock. The viral inoculum derived from serial passage without antiserum selection pressure was neutralized by two of the three mAbs. However, the viral inoculum derived from serial passage under antiserum selection pressure escaped neutralization by all three mAbs. Deep sequencing revealed the rapid acquisition of multiple mutations associated with antibody escape in the VSV-SARS-CoV-2-S that had been passaged in the presence of antiserum, including key mutations present in currently circulating Omicron subvariants. These data indicate that viral stock that was generated under polyclonal antiserum selection pressure better reflects the natural environment of the circulating virus and may yield more biologically relevant outcomes in phenotypic assays. Thus, mAb assessment assays that utilize a more genetically diverse, biologically relevant, virus stock may yield data that are relevant for prediction of mAb efficacy and for enhancing biosurveillance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Plasma confinement state classification in fusion power plants: Profile reflectometer and ensemble diagnostics

As Fusion Pilot Plants (FPPs) are increasingly viewed as within reach, many engineering challenges remain. Not many diagnostics are expected to be available in a reactor environment. Survivability, maintainability, and limited port space substantially restrict the number of FPP-relevant diagnostics. One remaining challenge is developing tools and devices to extract plasma state information necessary for controlling an FPP from a limited subset of diagnostics. This work is part of an overarching project to address this challenge. The specific diagnostic subset to be used in FPPs is still under debate. We take the approach of developing machine-learning-based tools for different significant plasma state parameters, using already known FPP-viable diagnostics. Previously we developed a plasma confinement mode classifier utilizing the Electron Cyclotron Emission (ECE) diagnostic. Here, we expand on this by developing a Profile Reflectometer (PR) based classifier with 97% test accuracy, and an ensemble model that combines the ECE and PR models into a single model, achieving 99% test accuracy.

Clark, Randall [Univ. of California, San Diego, CA↗
DOE OSTI2024

Data-Driven Surrogate Modeling with Microstructure-Sensitivity of Viscoplastic Creep in Grade 91 Steel

Abstract To support the development of advanced steel alloys tailored to withstand extreme conditions, it is imperative to account for the mechanical performance of components, while considering the influence of local microstructure on the macroscopic response. To this end, this study focuses on the development of microstructure-sensitive constitutive models for the mechanical response of Grade 91 steel exposed to extreme thermo-mechanical environments. Polynomial chaos expansion (PCE) surrogates are used to emulate high-fidelity polycrystal simulations of the viscoplastic response of Grade 91 steel as a function of the microstructure fingerprint (e.g., dislocations and precipitates). To cover a wide temperature–stress domain, two separate PCE surrogates—one that captures softening and the other that captures hardening behavior—are combined using another (sparse) Gaussian process regression model. The resulting constitutive creep surrogate model is integrated within the MOOSE finite element framework to simulate the intricate effects of microstructure, in particular MX-phase precipitates, on a component with a graded microstructure. Surrogate sensitivity analysis is applied to quantify the relevant impact of spatially varying microstructure on the creep response in a test-case involving a Grade 91 alloy with a prototypical weld.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Synthetic Digital Environments for Training Robots on Earth & Beyond [Poster]

Creating physical replicas of real-world environments to train robots for challenging outdoor tasks, whether constructing energy infrastructure like solar farms on Earth or on the Moon and Mars, is prohibitively expensive. This project will prototype a high-fidelity digital twin framework using NVIDIA IsaacSim to create realistic digital representations of robotic systems and their operating conditions, including varied terrains and environmental factors, allowing robots to learn and adapt in a faster, safer, and more affordable way to tackle unpredictable challenges in terrestrial and extraterrestrial applications. The Robotic Space Exploration (RoSE) Lab at Colorado School of Mines focused on the development and testing of synthetic digital twins to explore multi-physics interactions between robots and unstructured environments, with emphasis on lunar conditions such as deformable regolith, reduced gravity, and terrain-robot contact dynamics. The Industrialized Construction Innovation (ICI) team at National laboratory of the Rockies (NLR), simulated robotic apparatus and construction workflows using synthetic digital twins to inform real-world deployment, targeting application-driven use cases such as robotic construction of a scaled prototype of a photovoltaic energy infrastructure. Joint efforts between RoSE and ICI are continuing to explore how environment-scale multi-physics modeling and application-level robotic system simulation could be integrated to support robotic construction of energy infrastructure in highly unstructured environments, including scenarios relevant to the Lunar South Pole.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Examining the role of glycoside hydrolases in local rheology of Pseudomonas aeruginosa biofilms

Current research strategies in the treatment of biofilm infections have focused on dispersal, in which bacteria are made to vacate the extracellular polymeric substance (EPS) surrounding them and return to a planktonic state where antimicrobial treatments are more effective. Glycoside hydrolases (GHs), which cleave bonds in EPS polysaccharides, have been shown to promote dispersal in Pseudomonas aeruginosa biofilms. The dispersal mechanism is possibly due to GHs’ ability to directly release bacteria from the EPS, disrupt EPS’ ability to regulate the environment, or reduce overall mechanical stability. In this work, passive microrheology is used to examine the relevance of the last mechanism by exploring the effects of three GHs (α-amylase, cellulase, and xylanase) known to disperse P. aeruginosa on local biofilm viscoelasticity. Compared to control studies in wild-type strains, it is found that treatment with all three GHs results in statistically relatively less elastic and stiffer biofilms, indicating that changes to mechanical stability may be a factor in effective dispersal. Both cellulase and xylanase were observed to have the greatest impact in creating a less stiff and elastic biofilm; these GHs have been observed to be effective at dispersal in the published results. Each GH was further tested on biofilms grown with strains that produced EPS missing specific polysaccharide components. Cellulase specifically targeted Psl, which forms the major structural and mechanical backbone of the EPS, explaining its efficacy in dispersal. However, xylanase did not appear to exhibit any affinity to any polysaccharide within the EPS based on the microrheology results. Overall, these results suggest that the local microrheology of the biofilms is impacted by GHs and that may be one of the factors that is causing the ability of these therapeutics to enhance dispersal.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Automated Label‐Free Assay for Viral Detection and Inhibitor Screening via Biomembrane‐Functionalized Microelectrode Arrays

Most virus infection assays have indirect readout such as virus number following entry (e.g., PCR, cell lysis). While effective, these technologies are labor‐intensive, require specialized environments (e.g., sterile or RNA‐free), and detect later‐stage viral events like lysis or cell death, lacking sensitivity to early fusion events. To address these limitations, we present biologically relevant 2D membrane materials, host‐cell‐derived supported lipid bilayers (hcd‐SLBs), integrated with organic microelectrode arrays (OMEAs) for detection of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) fusion. By overexpressing angiotensin‐converting enzyme 2 (ACE2) receptors on the native membranes, the platform functions as a viral sensor capable of detecting virus pseudo particles (VPPs) through the late pathway. Additionally, hcd‐SLBs extracted from human lung epithelium expressing native ACE2 detect fusion events through the early pathway. The platform's utility as a drug‐screening tool is demonstrated by testing antibodies targeting either the ACE2 on the host membrane or the viral spike (S) proteins. To enhance the throughput, microfluidics are integrated for automation and OMEAs are incorporated within each channel, miniaturizing the testing units. This system supports high‐throughput data generation, automation, and scalability, providing an efficient platform for viral fusion detection that advances the study of pathogen‐host interactions and accelerates antiviral drug discovery.

Biology↗
DOE OSTI2024

Metalloproteomics Reveals Multi-Level Stress Response in Escherichia coli When Exposed to Arsenite

The arsRBC operon encodes a three-protein arsenic resistance system. ArsR regulates the transcription of the operon, while ArsB and ArsC are involved in exporting trivalent arsenic and reducing pentavalent arsenic, respectively. Previous research into Agrobacterium tumefaciens 5A has demonstrated that ArsR has regulatory control over a wide range of metal-related proteins and metabolic pathways. We hypothesized that ArsR has broad regulatory control in other Gram-negative bacteria and set out to test this. Here, we use differential proteomics to investigate changes caused by the presence of the arsR gene in human microbiome-relevant Escherichia coli during arsenite (AsIII) exposure. We show that ArsR has broad-ranging impacts such as the expression of TCA cycle enzymes during AsIII stress. Additionally, we found that the Isc [Fe-S] cluster and molybdenum cofactor assembly proteins are upregulated regardless of the presence of ArsR under these same conditions. An important finding from this differential proteomics analysis was the identification of response mechanisms that were strain-, ArsR-, and arsenic-specific, providing new clarity to this complex regulon. Given the widespread occurrence of the arsRBC operon, these findings should have broad applicability across microbial genera, including sensitive environments such as the human gastrointestinal tract.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Image-Driven Hybrid Structural Analysis Based on Continuum Point Cloud Method with Boundary Capturing Technique

Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure. The captured edge profiles are transformed into essential boundary conditions. This allows the construction of a strongly formulated boundary value problem (BVP), classified as the Dirichlet problem. Capturing boundary conditions from the digital image is novel, although a similar approach was applied to the point cloud data. It was shown that the CPCM is more efficient in this hybrid simulation framework than the weak-form-based numerical schemes. Unlike the finite element method (FEM), it can avoid aligning boundary nodes with regression points. A three-point bending test of a rubber beam was simulated to validate the developed technique. The simulation results were benchmarked against numerical results by ANSYS and various relevant numerical schemes. The technique can effectively solve the Dirichlet-type BVP, yielding accurate deformation, stress, and strain values across the entire problem domain when employing a linear strain model and increasing the number of CPCM nodes. In addition, comparative analysis with conventional displacement tracking techniques verifies the developed technique’s robustness. The proposed technique effectively circumvents the inherent limitations of traditional monitoring methods resulting from the reliance on physical gauges or target markers so that a robust and non-contact solution for remote structural health monitoring in real-scale infrastructures can be provided, even in unfavorable experimental environments.

Chemistry↗