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At least 1,459 records · Page 81

Catalyst and reactor design considerations for selective production of acids by oxidative cleavage of alkenes and unsaturated fatty acids with H 2 O 2

Oxidative cleavage of alkenes and unsaturated fatty acids with hydrogen peroxide gives an efficient and sustainable process to obtain mono- and di-acids for polymers and lubricants with fewer safety risks and less environmental impact than processes that utilize ozone or other inorganic oxidizers (e.g., permanganate, dichromate, etc.). Guided by insight into the mechanisms for competing reaction pathways (i.e., epoxidation of alkene on W–(η 2 -O 2 ) complexes vs. H 2 O 2 decomposition) and the apparent kinetics derived from kinetic experiments, here, we postulate that W-based heterogeneous catalysts can provide high performance and stable operations at low H 2 O 2 concentrations. Semi-batch reactors with continuous introduction of H 2 O 2 solutions offer the means to maintain low H 2 O 2 concentrations while providing sufficient quantities of H 2 O 2 to satisfy the reaction stoichiometry. We derived simple kinetic model equations for the epoxidation, ring-opening, oxidative cleavage, and oxidation steps and fit theses equations to batch experimental data to obtain kinetic parameters. Furthermore, this kinetic model describes the concentration profiles of reactant, oxidant, and products well as shown by agreement with experimental data. Further predictions of the optimal H 2 O 2 feed rate for semi-batch operation utilized by the proposed rate expressions and the reactor design equations suggest that low H 2 O 2 feed rate increases selectivity towards oxidative cleavage products and selective use of H 2 O 2 for oxidative cleavage pathway. Comparisons of oxidative cleavage of 4-octene in batch and semi-batch reactors show that semibatch reactors with optimized molar feed rates of H 2 O 2 increased oxidative cleavage product selectivities (76% to 99%; with an increase in butyric acid selectivity from 1% to 55%) and H 2 O 2 selectivity (3% to 30%). In addition, semibatch reaction conditions used avoid H 2 O 2 -mediated dissolution of W-atoms from the catalyst. Analysis of these findings suggest that solid oxide catalysts will be effective for continuous oxidative cleavage reactions if deployed within fixed-bed reactors that allow for distributed introduction of reactants and therefore low in situ concentrations of H 2 O 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Automated instant labeling chemistry workflow for real-time monitoring of monoclonal antibody N -glycosylation

With the transition toward continuous bioprocessing, process analytical technology (PAT) is becoming necessary for rapid and reliable in-process monitoring during biotherapeutics manufacturing. Bioprocess 4.0 is looking to build end-to-end bioprocesses that include PAT-enabled real-time process control. This is especially important for drug product quality attributes that can change during bioprocessing, such as protein N-glycosylation, a critical quality attribute for most monoclonal antibody (mAb) therapeutics. Glycosylation of mAbs is known to influence their efficacy as therapeutics and is regulated for a majority of mAb products on the market today. Currently, there is no method to truly measure N-glycosylation using on-line PAT, hence making it impractical to design upstream process control strategies. We recently described the N-GLYcanyzer workflow: an integrated PAT unit that measures mAb N-glycosylation within 3 hours of automated sampling from a bioreactor. Here, we integrated Agilent's Instant Procainamide (InstantPC) based chemistry workflow into the N-GLYcanyzer PAT unit to allow for nearly 10× faster near real-time analysis of mAb glycoforms. Furthermore, our methodology is explained in detail to allow for replication of the PAT workflow as well as present a case study demonstrating the use of this PAT to autonomously monitor a mammalian cell perfusion process at the bench scale to gain increased knowledge of mAb glycosylation dynamics during continuous biologics manufacturing using Chinese hamster ovary (CHO) cells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Modular droplet injector for sample conservation providing new structural insight for the conformational heterogeneity in the disease-associated NQO1 enzyme

Droplet injection strategies are a promising tool to reduce the large amount of sample consumed in serial femtosecond crystallography (SFX) measurements at X-ray free electron lasers (XFELs) with continuous injection approaches. Here, we demonstrate a new modular microfluidic droplet injector (MDI) design that was successfully applied to deliver microcrystals of the human NAD(P)H:quinone oxidoreductase 1 (NQO1) and phycocyanin. We investigated droplet generation conditions through electrical stimulation for both protein samples and implemented hardware and software components for optimized crystal injection at the Macromolecular Femtosecond Crystallography (MFX) instrument at the Stanford Linac Coherent Light Source (LCLS). Under optimized droplet injection conditions, we demonstrate that up to 4-fold sample consumption savings can be achieved with the droplet injector. In addition, we collected a full data set with droplet injection for NQO1 protein crystals with a resolution up to 2.7 Å, leading to the first room-temperature structure of NQO1 at an XFEL. NQO1 is a flavoenzyme associated with cancer, Alzheimer's and Parkinson's disease, making it an attractive target for drug discovery. Further, our results reveal for the first time that residues Tyr128 and Phe232, which play key roles in the function of the protein, show an unexpected conformational heterogeneity at room temperature within the crystals. These results suggest that different substates exist in the conformational ensemble of NQO1 with functional and mechanistic implications for the enzyme's negative cooperativity through a conformational selection mechanism. Our study thus demonstrates that microfluidic droplet injection constitutes a robust sample-conserving injection method for SFX studies on protein crystals that are difficult to obtain in amounts necessary for continuous injection, including the large sample quantities required for time-resolved mix-and-inject studies.

59 BASIC BIOLOGICAL SCIENCES↗

Elucidating the degradation mechanisms of Pt-free anode anion-exchange membrane fuel cells after durability testing

The development of anion-exchange membrane fuel cells (AEMFCs) has recently accelerated due to synergistic improvements yielding highly conductive membranes, stable ionomers, and enhanced alkaline electrocatalysts. However, cell durability, especially under realistic conditions, still poses a major challenge. Herein, we employ low-loadings of Pt-free Pd-based catalysts in the anode of AEMFCs and elucidate potential degradation mechanisms impacting long-term performance under conditions analogous to the real-world (high current density, H 2 –air (albeit CO 2 -free), and intermittent operation). Our high-performing AEMFCs achieve impressive performance with power densities approaching 1 W cm —2 and current densities up to 3.5 A cm —2 . Over a 200 h period of continuous operation in H 2 –air at a current density of 600 mA cm —2 , our model Pd/C–CeO 2 anode cell exhibits record stability (~30 μV h —1 degradation) compared to the literature and up to 6× better stability than our Pd/C and commercial Pt/C anode cells. Following an 8 h shutdown, the Pd/C–CeO 2 anode cell was restarted and continued for an additional 300 h with a higher degradation rate of ~600 μV h —1 . Thorough in situ evaluations and post-stability analyses provide insights into potential degradation mechanisms to be expected during extended operation under more realistic conditions and provide mitigation strategies to enable the widespread development of highly durable AEMFCs.

08 HYDROGEN↗

An overview of various critical aspects of low-cobalt/cobalt-free Li-ion battery cathodes

Cathodes of lithium-ion batteries (LIBs) significantly impact the environmental footprint, cost, and energy performance of the battery-pack. Hence, sustainable production of Li-ion battery cathodes is critically required for ensuring cost-effectiveness, environmental benignity, consumer friendliness, and social justice. Battery chemistry largely determines individual cell performance as well as the battery pack cost and life cycle greenhouse gas emission. Continuous manufacturing platforms improve production efficiency in terms of product yield, quality and cost. Spent-battery recycling ensures the circular economy of critical elements that are required for cathode production. Innovations in fast-charging LIBs are particularly promising for sustainable e-mobility with a reduced carbon footprint. This article provides an overview of these research directions, emphasizing strategies for low-cobalt cathode development, recycling processes, continuous production and improvement in fast-charging capability.

25 ENERGY STORAGE↗

Maximizing long-term biohydrogen production with Clostridium thermocellum for high solids conversion of lignocellulosic biomass

Biological hydrogen production from lignocellulosic biomass sustainably couples organic waste reduction with renewable energy generation. Efficient conversion is challenged by the structural complexity of lignocellulose and resulting recalcitrance to enzymatic degradation. Clostridium thermocellum natively breaks down biomass with highly effective hemi-/cellulases systems (i.e., cellulosomes) and generates hydrogen in anaerobic cultivation, creating a compelling platform for lignocellulosic biohydrogen production. Achieving commercially viable production rates requires balancing high biomass loading and throughput against uniform mixing conditions required for enzyme dispersion, pH and temperature control, and efficient hydrogen and metabolite removal in continuous operation. To address these barriers to process intensification, we implemented novel reactor and process designs for high-solids lignocellulosic biomass fermentations using the C. thermocellum KJC19-9 strain, genetically engineered for co-utilization of cellulose and hemicellulose sugars (i.e., xylose). Via computational fluid dynamics (CFD) modeling and experimental validation, we achieved a >50% improvement in biohydrogen production with an improved anchor-type impeller morphology, coupled to a threefold reduction in agitation rate. To further reduce rheological constraints and accumulation of toxic metabolites, we then transitioned the process to sequencing fed-batch operation. The resulting process generated 24.87 L H 2 L −1 from 160 g L −1 of deacetylated and mechanically refined (DMR)-pretreated corn stover biomass over 16 days while solubilizing >95% of influent cellulose and hemicellulose, setting a new performance benchmark for continuous production of biohydrogen from lignocellulose.

08 HYDROGEN↗

Large-scale experimental validation of thermochemical water-splitting oxides discovered by defect graph neural networks

Thermochemical water-splitting (TCH) based on 2-step thermal redox cycles in metal oxides is a promising approach to generating H 2 , but state-of-the-art (SOTA) CeO 2 has several practical limitations, which has motivated continued materials discovery efforts in this field. Here, in this study, we improve upon a SOTA defect graph neural network (dGNN) surrogate model's oxygen vacancy predictions and combine them with materials project phase diagrams to down-select and discover structurally diverse, experimentally known metal oxides whose TCH performance was previously unknown. Amongst twelve candidates selected based on our high-throughput screening and down-selection criteria, we achieved ∼80% accuracy in identifying materials with stable redox cycling and hydrogen production in stagnation flow reactor water-splitting experiments. Closer to 100% accuracy can be achieved if higher-accuracy, hybrid DFT-predicted vacancy formation energies were computed and used in lieu of the most uncertain dGNN-based screening predictions, as they correct false positives to true negatives. Notably, two discovered candidates, Sr 3 PrMn 2 O 8 and Ba 2 Fe 2 O 5 , display hydrogen yields greater than CeO 2 under specific redox conditions. In conclusion, these results demonstrate our ability to computationally predict and experimentally validate promising candidate TCH materials that have the potential to compete with CeO 2 .

08 HYDROGEN↗

Adaptive Fault Current-Limiting Control of MMC for Protection of Multiterminal HVDC Systems

A crucial requirement of the protection system for multi-terminal high-voltage DC (MTDC) transmission is that it is capable of selectively isolating the faulty area from the healthy part of the network using DC circuit breakers (DCCBs), while ensuring continuous operation of converter stations in the healthy part of the network. But in the half-bridge modular multilevel converters (HB-MMCs) based MTDC system, since HB-MMCs do not have fault current absorption capability, when a DC fault occurs, the rising fault currents can quickly reach the blocking threshold within a few milliseconds and disrupt the operation of the MMCs in the healthy part. To facilitate fault-ride-through capability of MTDC system, large DC reactors are often considered in series with DCCBs to reduce the rate of rise of the fault current and prevent blocking the MMCs in the healthy part of the DC networks; however, large DC reactors prohibitively increase the cost of the system, introduce stability issues and can create post-fault oscillations. This paper presents an adaptive fault current-limiting control method for MMCs to avoid their blocking and enable the continuous operation of the healthy part of MTDC systems. The method contains two parts: The first part is based on circulating current feedforward control, which emulates virtual reactors in each arm of an MMC, and is immediately activated when the fault current starts to increase to reduce the rate of rise of the fault current. The second part temporarily bypasses all the submodules when the fault current exceeds a preset threshold, complementing the fault current-limiting effect of the first part. Neither part requires fault detection signals, and they are automatically activated during faults. Simulation case studies of a four-terminal bipolar MMC-based high-voltage DC system are presented to demonstrate the effectiveness of the proposed control methods.

active fault current limiting↗

Conservation Voltage Reduction (CVR) via Two-Timescale Control in Unbalanced Power Distribution Systems

Voltage control devices are employed in power distribution systems to reduce the power consumption by operating the system closer to the lower acceptable voltage limits; this technique is called conservation voltage reduction (CVR). The different modes of operation for system’s legacy devices (with binary control) and new devices (e.g. smart inverters with continuous control) coupled with variable photovoltaic (PV) generation results in voltage fluctuations which makes it challenging to achieve CVR objective. This paper presents a two-timescale control of feeder’s voltage control devices to achieve CVR that includes (1)a centralized controller operating in a slower time-scale to coordinate voltage control devices across the feeder and (2) local controllers operating in a faster timescale to mitigate voltage fluctuations due to PV variability. The centralized controller utilizes a three-phase optimal power flow model to obtain the decision variables for both legacy devices and smart inverters. The local controllers operate smart inverters to minimize voltage fluctuations and restore nodal voltages to their reference values by adjusting the reactive power support. The proposed approach is validated using the IEEE-123 bus (medium-size) and R3-12.47-2 (large-size) feeders. It is demonstrated that the proposed approach is effective in achieving the CVR objective for unbalanced distribution systems.

optimal control, distributed generation, fluctuati↗

SN 2021adxl: A luminous nearby interacting supernova in an extremely low-metallicity environment

SN 2021adxl is a slowly evolving, luminous, Type IIn supernova with asymmetric emission line profiles, similar to the well-studied SN 2010jl. We present extensive optical, near-ultraviolet, and near-infrared photometry and spectroscopy covering ∼1.5 years post discovery. SN 2021adxl occurred in an unusual environment, atop a vigorously star-forming region that is offset from its host galaxy core. The appearance of Ly α and O II , as well as the compact core, would classify the host of SN 2021adxl as a “Blueberry” galaxy, analogous to higher redshift, low-metallicity, star-forming dwarf “Green Pea” galaxies. Using several abundance indicators, we find a metallicity of the explosion environment of only ∼0.1 Z ⊙ , the lowest reported metallicity for a Type IIn SN environment. SN 2021adxl reaches a peak magnitude of M r ≈ −20.2 mag and since discovery, SN 2021adxl has faded by only ∼4 magnitudes in the r band with a cumulative radiated energy of ∼1.5 × 10 50 erg over 18 months. SN 2021adxl shows strong signs of interaction with a complex circumstellar medium, seen by the detection of X-rays, revealed by the detection of coronal emission lines, and through multi-component hydrogen and helium profiles. In order to further understand this interaction, we model the H α profile using a Monte Carlo electron scattering code. The blueshifted high-velocity component is consistent with emission from a radially thin spherical shell resulting in the broad emission components due to electron scattering. Using the velocity evolution of this emitting shell, we find that the SN ejecta collide with circumstellar material of at least ∼5 M ⊙ assuming a steady-state mass-loss rate of ∼4 − 6 × 10 −3 M ⊙ yr −1 for the first ∼200 days of evolution. SN 2021adxl was last observed to be slowly declining at ∼0.01 mag d −1 , and if this trend continues, SN 2021adxl will remain observable after its current solar conjunction. Continuing the observations of SN 2021adxl may reveal signatures of dust formation or an infrared excess, similar to that seen for SN 2010jl.

Brennan, S. J. (ORCID:0000000313256235)↗

Dust supply to close binary systems

Binary systems can be born surrounded by circumbinary discs. The gaseous disc around either of the two stellar companions can have its life extended by the supply of mass arriving from the circumbinary disc. The objective of this study is to investigate the gravitational interactions exerted by a compact and eccentric binary system on the circumbinary and circumprimary discs, and the resulting transport of gas and solids between the disc components. We assume that the gas in the system behaves as a fluid, and we model its evolution by means of high-resolution hydrodynamical simulations. Dust grains are modelled as Lagrangian particles that interact with the gas and the stars. Our models indicate that significant fluxes of gas and dust proceed from the circumbinary disc towards the circumprimary disc. For the applied system parameters, grains of certain sizes are segregated outside the tidal gap generated by the stars. Consequently, the size distribution of the transported dust is not continuous, but presents a gap in the millimetre size range. In close binaries, the lifetime of an isolated circumprimary disc is found to be short, ~10 5 years, because of its low mass. However, because of the influx of gas from beyond the tidal gap, the disc around the primary star can survive much longer, ~10 6 years, as long as gas accretion from the circumbinary disc continues. The supply of solids and the extended lifetime of a circumbinary disc also aids in the possible formation of giant planets. Compared to close binary systems without a circumbinary disc, we expect a higher frequency of singleplanet or multiple-planet systems. Additionally, a planetesimal or debris belt can form in the proximity of the truncation radius of the circumprimary disc and/or around the location of the exterior edge of the tidal gap.

79 ASTRONOMY AND ASTROPHYSICS↗

Risk-averse optimal control of semilinear elliptic PDEs

In this paper, we consider the optimal control of semilinear elliptic PDEs with random inputs. These problems are often nonconvex, infinite-dimensional stochastic optimization problems for which we employ risk measures to quantify the implicit uncertainty in the objective function. In contrast to previous works in uncertainty quantification and stochastic optimization, we provide a rigorous mathematical analysis demonstrating higher solution regularity (in stochastic state space), continuity and differentiability of the control-to-state map, and existence, regularity and continuity properties of the control-to-adjoint map. Our proofs make use of existing techniques from PDE-constrained optimization as well as concepts from the theory of measurable multifunctions. We illustrate our theoretical results with two numerical examples motivated by the optimal doping of semiconductor devices.

97 MATHEMATICS AND COMPUTING↗

Kubernetes for the Deep Underground Neutrino Experiment Data Acquisition

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino experiment based in the USA which is expected to start taking data in 2029. DUNE aims to precisely measure neutrino oscillation parameters by detecting neutrinos from the LBNF beamline (Fermilab) at the Far Detector, 1,300 kilometres away, in South Dakota at the Sanford Underground Research Facility. The Far Detector will consist of four cryogenic Liquid Argon Time Projection Chamber detectors of 17 kT, each producing more than 1 TB/sec of data. The main requirements for the data acquisition system are the ability to run continuously for extended periods of time, with a 99% up-time requirement, and the functionality to record both beam neutrinos and low energy neutrinos from the explosion of a neighbouring supernova, should one occur during the lifetime of the experiment. The key challenges are the high data rates that the detectors generate and the deep underground environment, which places constraints on power and space. To overcome these challenges, DUNE plans to use a highly optimised C++ software suite and a server farm of about 110 nodes continuously running about two hundred multicore processes located close to the detector, 1.5 kilometres underground. Thirty nodes will be at the surface and will run around two hundred processes simultaneously. DUNE is studying the use of the Kubernetes framework to manage containerised workloads and take advantage of its resource definitions and high up-time services to run the DAQ system. Progress in deploying these systems at the CERN neutrino platform on the prototype DUNE experiments is reported.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

PHYSLITE - A new reduced common data format for ATLAS

The High Luminosity LHC (HL-LHC) era brings unprecedented computing challenges that call for novel approaches to reduce the amount of real and Monte Carlo-simulated data that is stored, while continuing to support the rich physics program of the ATLAS experiment. With the beginning of LHC Run 3, ATLAS introduced a new common data format, PHYS, that replaces most of the analysis-specific formats that were used in Run 2, and therefore reduces the disk storage significantly. ATLAS also launched the prototype of another common format, PHYSLITE, that is about a third of the size of PHYS. PHYSLITE will be the main format for ATLAS at the HL-LHC and aims to serve 80% of all physics analyses. To simplify analysis workloads and further reduce disk usage it is designed to largely replace user-defined analysis n-tuples and consequently contains pre-calibrated objects. Various forms of validations are in place to ensure correct functionality for users. Developments continue towards HL-LHC to improve the PHYSLITE format further.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

The MCNP ® 6 code: A decade of progress

After several years of effort involved in merging the Los Alamos National Laboratory MCNP5 and MCNPX codes, in 2013 the first production release of version 6 of the Monte Carlo N-Particle ® , or MCNP ® , code MCNP6.1 was distributed publicly. Since then, three significant releases have been issued: MCNP6.1.1beta in 2014, MCNP6.2 in 2018, and MCNP6.3 in 2023. While each release always contains new features, code enhancements, and bug fixes, each version has had a different primary focus, ranging from improved calculational efficiency to new powerful utilities and tools, to software modernization of the code base. With all that has been learned over the first decade of the MCNP6 code, continuous progress is being made toward a modernized, general-purpose Monte Carlo radiation transport code that remains a trusted resource for the global community of practitioners. This paper describes these first 10+ years of the MCNP6 code and its continually improving data libraries, and gives some insight into how the next decade is expected to unfold.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Identifying Modular Construction Worker Tasks Using Computer Vision

Modular construction is increasingly being seen as an attractive method for delivering building projects due to advantages in safety, quality, and lead-time. Despite these benefits, this method still relies heavily on human labor, which causes variability in factory assembly-line performance that can erode performance benefits of modular construction. Continuous improvement methods can alleviate some of these issues, but they also require continuous monitoring of human workers' performance. Due to limitations of manual time study and automated sensor-based monitoring methods, recently computer vision-based methods have gained momentum in identifying the activities of construction workers from the videos of onsite construction. Therefore, this paper explores the use of computer vision-based human activity recognition techniques to identify and classify worker activities in modular construction videos. Computer vision-based tracking method has been used to track the human workers in each frame, and Resnet-50 network has been used to classify the activity of tracked workers. Evaluation of this framework has achieved higher than 90% accuracy and recall in testing.

computer vision↗

Tracking Volumetric Units in Modular Factories for Automated Progress Monitoring Using Computer Vision

The construction industry is increasingly adopting off-site and prefabricated methods due to advantages offered in safety, quality, and lead time. Applying industrialized methods for plant management in offsite construction factories requires the collection of large volumes of production process data, which is a tedious task when performed manually. Recent attempts to automate this process have relied on sensor-based data collection methods which are susceptible to noise, expensive, and difficult to validate. Computer vision methods, however, enable process data collection from videos without the limitations of the other sensor-based methods. This technology has not been applied for offsite construction except in very few instances and therefore, this study proposes a novel method to reliably collect the production process data using computer vision method in near real-time from widely used surveillance cameras in offsite construction. The proposed method allows the user to annotate the workstations of interest on the video as ground truths and process these areas throughout the entire video to track the units entering and leaving stations, while continuously updating a near real-time schedule of the production line. This framework was validated by implementing on the surveillance videos of the production process of modular home manufacturing in a factory. The results consistently provided 100% accuracy, after denoising, for all the videos processed including 60 h of work for a station. The developed method enables real-time tracking of station performance, which can enable continuous improvement methods for factory management and resource allocation.

computer vision↗

Scenario Generation for Built Environment Decision Support under Uncertainty: Case Studies of Airflow Modeling and Climate-Resilient Infrastructure System Design

When confronted with unforeseen challenges, practicing informed decision making is crucial for enhancing resilience in the built environment. While scan-to-building information modeling (BIM) is a well-established approach for creating detailed digital representations of physical assets, its application in assessing and improving infrastructure resilience remains underexplored. This study addresses this gap by proposing a novel application of scan-to-BIM, namely, scan-to-BIM-to-digital twin (S-BIM-DT) workflow. By integrating reality capture and digital twin technologies, this workflow creates continuously updated and accurate digital representations of physical assets, enabling the generation of various scenarios. Unlike traditional methods, the S BIM-DT workflow facilitates continuous model refinement, supporting informed resilience strategies. By combining these technologies into a cohesive process, the workflow facilitates decision making under uncertainty, enabling stakeholders to evaluate and respond to various scenarios effectively. We demonstrate the implementation of the S-BIM-DT workflow through two use cases that highlight its capability to enhance resilience at different scales. The first use case involves the Combined Transportation, Emergency, and Communications Center (CTECC) in Austin, Texas. BIM-enriched computational fluid dynamics (CFD) modeling simulates airflow and develops alternative scenarios for optimizing the heating, ventilation, and air conditioning (HVAC) systems. This approach enhances resilience against airborne health threats in a postCOVID context. The second use case focuses on designated areas within Beaumont, Texas, as part of the Southeast Texas Urban Integrated Field Laboratory (SETx-UIFL) research. By developing inundation maps to assess extreme weather events, this modeling aids in preparedness efforts and informs the development of climate-resilient infrastructure in vulnerable neighborhoods. Results indicate that the S-BIM-DT workflow effectively generates scenarios that enhance resilience in the built environment by facilitating informed decision making. Furthermore, this study serves as a bridge between advanced scan-to-BIM methodologies and the practical strategies needed to improve built infrastructure resilience.

Built environment↗