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At least 1,081 records · Page 60

Conceptual range estimation for total cost of ownership of modular process‐intensified chemical plants

Abstract Chemical companies have used modularization to reduce capital costs and project timelines, putting capital to work faster and lowering the risk of entering new markets. Nevertheless, the impacts of using modularization along with advanced technologies, such as process intensification, have not yet been fully realized, often due to the uncertain business risks associated with their implementation. Therefore, new methods are needed for quantifying the impact of modular chemical process intensification (MCPI) on the capital and operating costs of chemical plants to help build a business case for this novel approach. This article presents a new conceptual range estimation technique for total cost of ownership that addresses the deficiencies of existing methods for quantifying MCPI impacts. The incorporation of percentage range estimates was employed to allow for adaptability across various cost and size scales. This work also begins to elucidate how chemical engineering and construction firms can benefit from MCPI and identifies barriers that inhibit MCPI applications in the chemical industry.

Alhamouri, Khaled I.↗

Terahertz Dirac Hyperbolic Metamaterial

Hyperbolic metamaterials (HMMs) are engineered materials with a hyperbolic isofrequency surface, enabling a range of interesting phenomena and applications including negative refraction, enhanced sensing, and subdiffraction imaging, focusing, and waveguiding. Existing HMMs primarily work in the visible and infrared spectral range due to the inherent properties of their constituent materials. Here, we demonstrate a THz-range Dirac HMM using topological insulators as the building blocks. Here, we find that the structure houses up to three high-wavevector volume plasmon polariton (VPP) modes, consistent with transfer matrix modeling and effective medium theory calculations. The VPPs have mode indices greater than 100, significantly larger than observed for VPP modes in HMMs made from metals or doped semiconductors while maintaining comparable quality factors. We attribute these properties to the two-dimensional Dirac nature of the electrons occupying the topological insulator surface states. Because these are van der Waals materials, these structures can be grown at a wafer-scale on a variety of substrates, allowing them to be integrated with existing THz structures and enabling next-generation THz optical devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Mitigating urban heat island and enhancing indoor thermal comfort using terrace garden

The United Nations advocates for sustainable urban planning and design, emphasizing green infrastructure initiatives to mitigate urban heat island effects and enhance the resilience and livability of cities globally. To address urban heat challenges, a study was conducted in Chennai, India, from April to June 2023. The study focused on assessing temperature dynamics on a building's terrace by comparing a well-maintained garden area with an exposed region. Temperature and humidity sensors were deployed in both the garden and exposed areas of the terrace, as well as within rooms beneath it, to monitor hourly temperature fluctuations. The findings indicate a significant reduction in internal room temperatures in areas with rooftop gardens, ranging from 4 to 11 °C, depending on the time of year and sun's position, compared to rooms with fully exposed roof configurations. Additionally, simulation studies were performed to validate these findings, suggesting that optimizing the distribution of soil beds and plant density across the roof could yield an additional temperature reduction of 3–4 °C, resulting in an overall difference of up to 14–15 °C. The study highlights the efficacy of rooftop gardens in providing cooling effects during daylight hours and maintaining temperature parity post-sunset. Through analysis of sensor data, the research elucidates the intricate relationship between green infrastructure and thermal comfort, offering insights for energy-efficient building design and resilient urban planning. The findings underscore the potential of rooftop gardens in fostering a more comfortable, energy-efficient, and sustainable urban living environment.

54 ENVIRONMENTAL SCIENCES↗

Assessment of the Material Performance for TCR Applications (Progress Report)

The objective of Argonne’s materials research activities for the Transformational Challenge Reactor (TCR) program is to improve the understanding of material properties from additive manufacturing with the focus on understanding the creep and fatigue properties of additively manufactured materials. The material work conducted by Argonne provides support in developing and qualifying advanced materials and manufacturing processes to allow for innovative reactor design and licensing for the TCR. In FY 2020, Oak Ridge National Laboratory (ORNL) provided six ASTM standard sized creep specimens of additively-manufactured 316L stainless steel (AM 316L SS) for evaluation of the effect of post-build heat treatment on the creep property. Two different teat treatments were conducted on the as-built specimens, namely 1050°C/1h and 650°C/1h before creep testing. All specimens were creep tested at 550°C and 275 MPa to understand the effect of heat treatments. We have also received AM 316L SS printed in various forms including rods and sheets, wrought 316 L SS (as a reference material), and AM IN718 for tensile, creep and fatigue testing. Both ASTM standard sized specimens and subsized specimens are being fabricated or tested to contribute to a comprehensive mechanical property database of AM materials and to provide location-specific property data to the TCR digital platform. Mechanical testing at Argonne is conducted to ASME NQA-1 or its lab equivalent for quality assurance. This report presents the results of the creep tests at 550°C and microstructural characterization for the assessment of the effect of the post-build heat treatment on the creep behavior of AM 316L SS. It also discusses ongoing efforts on obtaining location-dependent properties of AM 316L SS.

36 MATERIALS SCIENCE↗

Cradle-to-Grave Lifecycle Analysis of U.S. Light-Duty Vehicle-Fuel Pathways: A Greenhouse Gas Emissions and Economic Assessment of Current (2020) and Future (2030-2035) Technologies

This study provides a comprehensive life cycle analysis (LCA), or cradle-to-grave (C2G) analysis, of the cost and greenhouse gas (GHG) emissions of a variety of vehicle-fuel pathways, the levelized cost of driving (LCD) and cost of avoided GHG emissions. The C2G analysis assesses light duty midsize sedans and small sport utility vehicles (SUVs) across a variety of vehicle-fuel technology pathways, including conventional internal combustion engine vehicles (ICEVs), flexible hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs) with varying vehicle ranges, and fuel cell electric vehicles (FCEVs). Coming at a timely manner, given the marked increase, since 2016, in climate aspirations announced by governmental institutions and private firms both in the US and across the globe, this analysis builds on a previous comprehensive life cycle analysis, updating that study’s 2016 assumptions and methods (Elgowainy et al. 2016). These updates incorporate technological advances and changes in energy supply sources that have emerged during the intervening period. Utilizing these updated assumptions and methods, alongside more recent data, the present report accounts for a broader range of vehicle technologies and considers both current (2020) and expected future (2030-2035) conditions. Reflecting increased research interest in synthetic liquid fuels produced using renewable low-carbon electricity and CO 2 sources, electro-fuels (a.k.a. e-fuels) were added to the potential future fuel technologies that are evaluated. This study takes a “pathway” approach rather than a “scenario” approach; hence distinct, technically feasible, routes or sequences of processes starting with one or more feedstocks and ending with an intermediate or a final product are examined, not necessarily constrained by practical feedstock, economic, policy, and market considerations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Summary of Stack Tests at TA-16-Bldg 205: Horizontal Duct on WETF Roof

In late 2023 and early 2024, testing was done at the Weapons Engineering Tritium Facility (WETF) rooftop exhaust duct to verify that the stack sample location was in compliance with the ANSI standard for stack sampling, ANSI N13.1-1999. This ANSI standard is called out by the Clean Air Act rules for airborne radionuclide emissions from DOE facilities, 40 CFR 61 Subpart H. This sample location is designated 16020504, indicating Technical Area 16, Building 0205. The rooftop duct formerly discharged to Exhaust Stack 04, but in 2007 it was re-routed to exhaust out Building 0450, Exhaust Stack 05. For continuity, we have kept the 16020504 designation to represent this duct sample location.

54 ENVIRONMENTAL SCIENCES↗

Pipe-in-Pipe by Rapid, Continuous, Smart Alloy Coating

Our team has developed a comprehensive solution for performing in-situ structural repairs on metal piping and pipelines. This is based on a multifaceted and integrated (experimental and computational) approach, that uses a slurry-based coating and our patented high temperature sintering process, to build new metal in a pipe-in-pipe configuration. This can be utilized in either a complete pipe-in-pipe embodiment, or for patch repairs.

42 ENGINEERING↗

Theoretical studies of chemical reactions related to the formation and growth of polycyclic aromatic hydrocarbons (PAH) and molecular properties of their key intermediates (Final Progress Report)

The formation mechanisms of polycyclic aromatic hydrocarbons, (PAHs) – organic molecules carrying fused benzene rings – are of great interest to scientists and engineers due to their importance in combustion chemistry and astrochemistry. On Earth, PAHs are largely produced in incomplete combustion of fossil fuel and are considered as critical precursors to unwanted soot particles leading to combustion inefficiency and causing air pollution along with detrimental health effects. Simple PAH molecules initially formed in the gas phase, are further involved in a build-up process in combustion flames leading to larger PAH, bowl-shaped nanostructures, fullerenes, and solid-phase species including carbonaceous dust, graphene particles, and soot. In deep space, PAH and their derivatives are potential key intermediates and nucleation sites leading eventually to carbonaceous nanoparticles (“interstellar grains”). Therefore, the understanding of the key processes in the synthesis of PAHs along with their precursors and their degradation mechanisms in combustion systems and in interstellar, circumstellar, and planetary atmospheric environments will provide critical insights into how complex aromatic structures, carbonaceous nanoparticles, and fullerenes are formed and destroyed. Achieving this understanding is an important step in the development of the efficient combustion processes and of the ecofriendly devices with reduced environmental pollution as well as technological strategies for the production of hydrogen and solid carbon through thermal or plasma-assisted pyrolysis of natural gas and biomass. Also, the understanding of the key processes of PAH and soot growth will help in our comprehension of chemical evolution in the universe. Detailed information on the mechanisms and reliable rate constants of the key elementary chemical reactions involved in PAH formation and destruction processes and in inception of soot particles is often missing, with the main deficiencies being the absence of temperature- and pressure-dependent rate constants for the broad range of conditions occurring in various terrestrial and interstellar processes and the lack of data on the reaction products and their branching ratios. Complementary to experimental studies, these gaps in knowledge can be filled by using quantum chemical calculations of reaction potential energy surfaces providing us with accurate energies of reaction products, intermediates, and transition states, revealing the reaction mechanism, and giving the molecular properties required to compute rate constants for relevant reaction steps and product branching ratios using the RRKM-Master Equation (ME) method. Molecular dynamics (MD) simulations can be used in cases when a reaction rate cannot be properly described by statistical theories. During the terminal renewal project period we employed these ab initio/RRKM-ME and MD approaches to complete our studies on several key reactions relevant to the formation/growth of PAH and inception of soot particles including (1) the reaction mechanism and kinetics of the resonance stabilized fulvenallenyl radical with propargyl and C 3 H 4 isomers; (2) the reaction mechanism and kinetics for the C + indene and C 2 + styrene reactions producing naphthyl or azulenyl radicals in low-temperature environments; (3) the MD study of non-equilibrium dimerization of acepyrene and coronene and its radical. The information derived from our theoretical calculations contributed to a better fundamental understanding of the reaction mechanisms and provide missing critical kinetic data to improve combustion models of hydrocarbon fuels and astrochemical models of the growth of carbonaceous molecules and particles in cold molecular clouds, circumstellar envelopes, and planetary atmospheres.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Application of FREMES to Characterize and Sort Soil During Site Remediation - 20182

During decommissioning of buildings and the related release of nuclear sites, large amounts of bulk materials such as soil must be measured for clearance. These measurements are required to demonstrate that for example potentially contaminated soil is below the respective radiological limits. Doing so also helps to minimize the amount of bulk material which has to be disposed of as nuclear waste. NUKEM Technologies Engineering Services GmbH (NUKEM) applies their FREMES technology in a project at the former fuel element factory of FBFC International at Dessel, Belgium. The purpose is to measure and check for potential contamination within the excavated soil. The project started in 2017 with design and procurement of the necessary equipment. In October 2017 the FREMES system was installed at the site. After installation and an extensive test program the FREMES facility went fully into operation in July 2018. Original foreseen to measure between 4.000 and 8.000 tons of material, it has currently (mid 2019) measured 23.600 tons. It is expected that another 5.000 tons still have to be measured before the project successfully ends. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Tiered Energy Use in Buildings (TEB)

This tool creates a set of building energy simulations and organizes them according to a tier-based model representing circuits that will be on or off depending on a microgrid controller (not included in this software). This Python based code uses the open-source RCBuildingSimulator that is under an amended MIT license. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-2528 O

Villa, Daniel↗

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗

Development, verification, and validation of comprehensive acoustic fluid-structure interaction capabilities in an open-source computational platform

The acoustic fluid-structure interaction (FSI) formulation is a practical numerical approach for the seismic analysis of fluid-filled tanks. However, there are no verification and validation studies reported in the literature that demonstrate the ability of an acoustic FSI numerical model to predict responses important to structural and mechanical design for intense translational and rotational earthquake inputs. Herein, an acoustic FSI formulation is implemented in the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE), and is formally verified and validated using analytical solutions and code-to-code verification, and experimental data, respectively. The analytical solutions are for small amplitude, unidirectional seismic inputs. The code-to-code verification utilizes a previously verified and validated Arbitrary Lagrangian-Eulerian (ALE) numerical model in the commercial finite element code LS-DYNA. The validation studies utilize a comprehensive data set assembled from results of 3D earthquake-simulator tests of a fluid-filled vessel. The acoustic numerical model in MOOSE is verified and validated for hydrodynamic pressures and support reactions except for cases that involve significant convective response. For small amplitude inputs, numerically predicted wave heights match those of the analytical solutions. The numerical model is not verified and validated for wave height calculations under intense 3D seismic inputs. The run times for the acoustic FSI simulations in MOOSE are an order of magnitude, or more, shorter than for the corresponding ALE simulations in LS-DYNA. The utility of the MOOSE acoustic FSI implementation is demonstrated by seismic analysis of a building equipped with a fluid-filled, advanced nuclear reactor.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures

Abstract Part quality manufactured by the laser powder bed fusion process is significantly affected by porosity. Existing works of process–property relationships for porosity prediction require many experiments or computationally expensive simulations without considering environmental variations. While efforts that adopt real-time monitoring sensors can only detect porosity after its occurrence rather than predicting it ahead of time. In this study, a novel porosity detection-prediction framework is proposed based on deep learning that predicts porosity in the next layer based on thermal signatures of the previous layers. The proposed framework is validated in terms of its ability to accurately predict lack of fusion porosity using computerized tomography (CT) scans, which achieves a F1-score of 0.75. The framework presented in this work can be effectively applied to quality control in additive manufacturing. As a function of the predicted porosity positions, laser process parameters in the next layer can be adjusted to avoid more part porosity in the future or the existing porosity could be filled. If the predicted part porosity is not acceptable regardless of laser parameters, the building process can be stopped to minimize the loss.

42 ENGINEERING↗

Automated Strain Construction for Biosynthetic Pathway Screening in Yeast

Automation accelerates the Design-Build-Test-Learn (DBTL) cycle for synthetic biology; however, most strain construction pipelines lack robotic integration. Here, in this study, we present the workflow design and source code for a modular, integrated protocol that automates the Build step in Saccharomyces cerevisiae. We programmed the Hamilton Microlab VANTAGE to integrate off-deck hardware via its central robotic arm, enabling automated steps that increased throughput to 2,000 transformations per week. We developed a user interface with the Hamilton VENUS software to support on-demand parameter customization. As a proof of concept, we screened a gene library in an engineered yeast strain producing verazine, a key intermediate in the biosynthesis of steroidal alkaloids. Our pipeline rapidly identified pathway bottlenecks and genes that enhanced verazine production by 2.0- to 5-fold. This technical note provides resources for synthetic biologists designing yeast workflows for biofoundries to screen libraries for pathway discovery/optimization, combinatorial biosynthesis, and protein engineering.

automation↗

An automated approach to the alignment of compound refractive lenses

Compound refractive lenses (CRLs) are established X-ray focusing optics, and are used to focus the beam or image the sample in many beamlines at X-ray facilities. While CRLs are quite established, the stack of single lens elements affords a very small numerical aperture because of the thick lens profile, making them far more difficult to align than classical optical lenses that obey the thin-lens approximation. This means that the alignment must be very precise and is highly sensitive to changes to the incident beam, often requiring regular readjustments. Some groups circumvent the full realignment procedure by using engineering controls ( e.g. mounting optics) that sacrifice some of the beam's focusing precision, i.e. spot size, or resolution. While these choices minimize setup time, there are clear disadvantages. This work presents a new automated approach to align CRLs using a simple alignment apparatus that is easy to adapt and install at different types of X-ray experiments or facilities. This approach builds on recent CRL modeling efforts, using an approach based on the Stochastic Nelder–Mead (SNM) simplex method. This method is outlined and its efficacy is demonstrated with numerical simulation that is tested in real experiments conducted at the Advanced Photon Source to confirm its performance with a synchrotron beam. This work provides an opportunity to automate key instrumentation at X-ray facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration

This paper proposes novel noise-free Bayesian optimization strategies that rely on a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain the ease of implementation of the classical GP-UCB algorithm, but the additional random exploration step accelerates their convergence, nearly achieving the optimal convergence rate. Furthermore, to facilitate Bayesian inference with intractable likelihoods, we propose to utilize optimization iterates for maximum a posteriori estimation to build a Gaussian process surrogate model for the unnormalized log-posterior density. We provide bounds for the Hellinger distance between the true and the approximate posterior distributions in terms of the number of design points. We demonstrate the effectiveness of our Bayesian optimization algorithms in nonconvex benchmark objective functions, in a machine learning hyperparameter tuning problem, and in a black-box engineering design problem. The effectiveness of our posterior approximation approach is demonstrated in two Bayesian inference problems for parameters of dynamical systems.

Bayesian inference↗

Thermal Conductivity of AM Fabricated Stainless Steel 316L Part

Thermal properties are an integral part of many diverse engineering applications, and additive manufacturing (AM), particularly laser powder bed fusion (LPBF) is shown to affect thermal properties due to the laser processing parameters. For 316L stainless steel, there is little prior research to determine the effects of the process on thermal properties. In this work, the temperature gradient is shown to create uniform, chess board-like distributions of grains in the build direction. These zones create dislocations which were visualized with EBSD techniques. Processing parameters cause hierarchal grain size distribution, with localized concentrations of small grains and large grains. Thermomechanical stresses in the rapid solidification increases dislocation density during grain formation. Previous research shows a higher density of dislocations decreases local thermal conductivity. Local and bulk thermal conductivity are shown in this work to have statistically lowered values to an average of 10-12 W/m-K compared to 14 W/m-K for conventional 316L.

36 MATERIALS SCIENCE↗

Construction Methodology Transformation for the Benefit of Workforce Development

Construction is a key economic engine driving both national and global economies. While manual, onsite construction methods dominate the U.S. construction industry, a major shift towards offsite methods has been underway due to its efficiency, speed, and potential cost savings. The workforce necessary for offsite construction growth does not exist in its current form because of the focus on onsite methodologies and the lack of exposure to offsite building methods at all levels of a student’s learning journey. The growth of the U.S. construction industry and competitiveness in an increasingly global construction market over the coming decades can only be supported by a dramatic increase in the use of offsite methods, which requires ramping up workforce training for certain skillsets. The goal of Construction Methodology Transformation for the Benefit of Workforce Development was to understand the opportunities and barriers in both education and industry and to identify best practices for offering curriculum and training to educators, industry, and students that would support skills needed for careers in offsite construction. Our team proposed combining three offsite construction workforce development needs: content development, exposure and training, and job placement - under a single Platform model that would increase experience and career opportunities for students and help match them with potential industry members. Through our proposed solution we expected to see: developed offsite curriculum being utilized by educators and students; an increase in the identification of construction technology and offsite construction methods; an average increase in knowledge gain of at least 25% after participation in pilots; better equipped candidates who are prepared for jobs in offsite construction; and a beta workforce development platform that helps build more pathways for students looking for careers in offsite construction. The two pilots included almost 250 students and resulted in an average knowledge gain of 37 percent. Our research has identified areas of opportunity, for both education and industry to make collaborative training programs more efficient and successful. The chosen techniques for this program are extremely effective when both the school and factory have solid processes and cultures in place to accept students into training programs. This project serves as an important stepping stone to industrywide collaboration to move workforce development for offsite construction forward across the country. With continued collaboration programs like this can provide much needed early exposure and training in offsite construction and we can begin to fill important positions for the future of construction.

99 GENERAL AND MISCELLANEOUS↗