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At least 1,081 records · Page 60

Sustainable hydrogen manufacturing via renewable-integrated intensified process for refueling stations

The widescale consumer adoption of hydrogen fuel cell electric vehicles (HFCEVs) is currently hindered by the high cost of small-scale hydrogen generation and the lack of extensive hydrogen refueling infrastructure. Natural gas-based hydrogen is cheaper when produced in large volumes but is also associated with high CO 2 emissions. To counter these challenges, we propose a hybrid approach where both natural gas and renewables are integrated in a synergistic manner using a dynamic process intensification technology that can be deployed on-site for meeting local demands of refueling stations. The technology is based on sorption enhanced steam methane reforming (SE-SMR) that utilizes a combination of reaction with in-situ CO 2 adsorption for enhancing process modularity, productivity and efficiency thereby outperforming conventional SMR at small scale. We develop a mixed integer linear programming (MILP)-based optimization framework for simultaneous design and scheduling of the SE-SMR process. The simultaneous optimization provides a synergistic combination whereby the renewables allow sustainable hydrogen manufacturing and the dynamic SE-SMR allows optimal use of the intermittency of the renewables. The U.S. nationwide analysis indicates that for futuristic renewable prices and a hydrogen production capacity of 2 ton/day, hydrogen can be produced at 50% less cost compared to the current cost of small-scale hydrogen generation. Finally, the city-wise analysis with varying hydrogen demand shows that even with just 5% HFCEV market penetration level, hydrogen production cost less than $3/kg can be obtained at small scales across the United States with even cheaper hydrogen for large cities.

08 HYDROGEN↗

Mechanical properties, strain hardening, and fracture behavior of ultrasonic additively manufactured Zircaloy-4 after low-temperature neutron irradiation

Ultrasonic additive manufacturing (UAM) is a solid-state, layer-by-layer advanced manufacturing process that has the potential to create custom spatially controlled composites with embedded wires and sensors for nuclear component manufacture. For this work, to assess the feasibility of using UAM for nuclear-relevant materials research, the technique was used to produce a 3.5-mm-thick Zircaloy-4 plate for irradiation testing. The UAM Zircaloy-4 specimens were irradiated in the High Flux Isotope Reactor at a target irradiation temperature of 117 °C to 2.9 displacements per atom (dpa) to assess differences in irradiation-hardening behavior as a function of alloy processing path. The UAM and reference baseplate (BP) materials increased in yield strength by 372±27 MPa and 346±21 MPa, respectively, and both suffered significant reductions in uniform and total elongation attributed to irradiation hardening at low-temperature. Although the materials had similar nanoscale defect structures, including nanoscale black dot/loop features and strain-induced dislocation channels, the UAM material’s processing-related defects resulted in accelerated strain localization and failure as demonstrated by lower post-irradiation uniform elongation of UAM specimens (0.5 %) compared to BP (1.5 %) material. The UAM material also showed considerable anisotropy in mechanical response due to crack propagation along weld boundaries, resulting in differences in strength & ductility when tested parallel and perpendicular to the prior UAM build orientation. Therefore, although the fundamental irradiation response of UAM-processed Zircaloy-4 was phenomenologically comparable to that of BP reference material, additional optimization of the UAM processing is needed to produce irradiation-resistant and nuclear-relevant materials.

Digital image correlation↗

Quantification of the Impact of Acidified Brine on Fracture-Matrix Transport in a Naturally Fractured Shale Using in Situ Imaging and Modeling

Understanding flow, transport, chemical reactions, and hydromechanical processes in fractured geologic materials is key for optimizing a range of subsurface processes including carbon dioxide and hydrogen storage, unconventional energy resource extraction, and geothermal energy recovery. Flow and transport processes in naturally fractured shale rocks have been challenging to characterize due to experimental complexity and the multiscale nature of quantifying continuum scale descriptions of mass exchange between micrometer-scale fractures and nanometer-scale pores. In this study, we use positron emission tomography (PET) to image the transport of a conservative tracer in a naturally fractured Wolfcamp shale core before and after the core was exposed to low pH brine conditions. Image-based experimental observations are interpreted by fitting an analytical transport model to fracture-containing voxels in the core. Results of this analysis indicate subtle increases in matrix diffusivity and a slightly more uniform fracture velocity distribution following exposure to low pH conditions. These observations are compared with a multicomponent one-dimensional reactive transport model that indicates the capacity for a 10% increase in porosity at the fracture-matrix interface as a result of the low pH brine exposure. This porosity change is the result of the dissolution of carbonate minerals in the shale matrix to low pH conditions. Further, this image-based workflow represents a new approach for quantifying spatially resolved fracture-matrix transport processes and provides a foundation for future work to better understand the role of coupled transport, reaction, and mechanical processes in naturally fractured rocks.

58 GEOSCIENCES↗

Stochastic scheduling for commercial building cooling systems: considering uncertainty in zone temperature prediction

Here, this paper presents the first attempt to address the uncertainty in zone temperature prediction with stochastic optimization. The uncertain zone temperature is a process uncertainty and has not been considered in the existing stochastic optimization for building control. To fill this gap, we proposed a novel formulation of stochastic optimization to handle process uncertainty in building control. Specifically, we first examined the accuracy of a typical linear model for predicting zone temperature. We then formulated the scheduling of the building cooling system as a stochastic optimization problem over a 24-hour look-ahead period to minimize the electricity cost of the studied building cooling system. After that, we applied the proposed stochastic load scheduling (SLS) to a direct expansion (DX) cooling system that serves a medium office building. Through simulation with a detailed building energy simulation software, EnergyPlus, we evaluated the operational cost and the thermal comfort compared with a deterministic load scheduling. The operation cost of scheduling was found to vary with the level of zone temperature prediction uncertainty. The proposed SLS can mitigate the impacts of uncertain zone temperature predictions on both operational cost and thermal comfort. The evaluation results indicate that the proposed SLS works better when the uncertainty level is more significant.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Fast decay of classification error in variational quantum circuits

Variational quantum circuits (VQCs) have shown great potential in near-term applications. However, the discriminative power of a VQC, in connection to its circuit architecture and depth, is not understood. To unleash the genuine discriminative power of a VQC, we propose a VQC system with the optimal classical post-processing—maximum-likelihood estimation on measuring all VQC output qubits. Via extensive numerical simulations, we find that the error of VQC quantum data classification typically decays exponentially with the circuit depth, when the VQC architecture is extensive—the number of gates does not shrink with the circuit depth. This fast error suppression ends at the saturation towards the ultimate Helstrom limit of quantum state discrimination. On the other hand, non-extensive VQCs such as quantum convolutional neural networks are sub-optimal and fail to achieve the Helstrom limit, demonstrating a trade-off between ansatz complexity and classification performance in general. To achieve the best performance for a given VQC, the optimal classical post-processing is crucial even for a binary classification problem. To simplify VQCs for near-term implementations, we find that utilizing the symmetry of the input properly can improve the performance, while oversimplification can lead to degradation.

Zhang, Bingzhi↗

Self-consistent optimization of the z expansion for B -meson decays

We discuss the self-consistency imposed by the analyticity of regular parts of form factors, appearing in the z expansion for semileptonic B -meson decays, when fitted in different kinematic regions. Relying on the uniqueness of functions defined by analytic continuation, we propose four metrics which measure the departure from the ideal analytic self-consistency. We illustrate the process using Belle data for B → D ℓ ν ℓ with the two kinematic regions chosen as the five low- z and the five high- z bins. For this specific example, the metrics provide consistent indications that some choices (order of truncation, Boyd-Grinstein-Lebed or Bourrely-Caprini-Lellouch) made in the form of the z expansion can be optimized. However, other choices ( z origin, location of isolated poles and threshold constraints) appear to have very little effect on these metrics. On the other hand, changing the kinetic regions affects the results and should also be considered in the optimization process. We briefly discuss the implication for optimization of the z expansion for nucleon form factors relevant for neutrino oscillation experiments. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Cost-optimal selection of pH control for mineral scaling prevention in high recovery reverse osmosis desalination

Conference proceedings for Foundations of Computer Aided Process Design (FOCAPD) 2024 that details a cost optimization model for pH control to prevent mineral scaling in high recovery reverse osmosis. This work applies a framework for incorporating detailed water chemistry predictions such as precipitation, pH change, and mineral scaling tendencies with process-scale optimization. It demonstrates that the acid choice for pH control can have a significant impact on pretreatment costs.

Amusat, Oluwamayowa↗

Microwave Debinding of Ceramics Produced by Additive Manufacturing

Microwave-assisted debinding offers an innovative approach to processing 3D-printed engineering ceramics. Ceramic parts produced with photopolymer-based additive manufacturing techniques require thermal debinding to remove polymers that bind ceramic particles during the printing process. Microwave-assisted debinding significantly reduces processing times and offers energy savings over conventional thermal treatments due to higher heating rates and more uniform heating. Finite-element modeling of the debinding process allows for the optimization and prediction of temperature distribution, stress development, and potential defects, leading to improved process control and material quality.

36 - MATERIALS SCIENCE↗

High-Temperature Multi-Process Sensor Development and Demonstration at a Full-scale PC Combustion System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution of wall conditions in utility boilers. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of a coal-fired utility boiler in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Advanced Sensors, Corrosion, Ash Deposition, Optim↗

Operation Optimization Using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

Kim, Junyung↗

Improved Manufacturability and Throughput of Ultra- Transparent, Super-Insulating Aerogels

AeroShield Materials produces a novel silica aerogel material with exemplary thermal performance and unprecedented optical clarity, offering the potential for super-insulating fenestration and insulated glass to reduce thermal losses in the built environment by billions of dollars every year. One of the most significant challenges facing AeroShield today is the total amount of time that is required to produce large monolithic aerogel samples. This overall process can require as much as 144 hours total, which can significantly hinder scale-up to an economically viable product. The purpose of this Phase 1 research was to continue development of our novel aerogel manufacturing process to reduce material processing time by up to 10x, greatly improving product throughput and reducing cost. AeroShield’s manufacturing process can be divided into 4 major stages, each with their own distinct set of parameters and time requirements. Under this Phase 1 award, AeroShield was able to identify and optimize a number of these parameters, including molding materials, solvent rinse conditions, critical point drying time, and annealing conditions. AeroShield also performed thorough analyses on how these optimized parameters affected important final characteristics of the gels, including optical clarity, thermal conductivity, and dimensional stability. This campaign culminated in the production of laboratory scale aerogel samples using significantly lower process times of both 36 and 18 total hours, which represent Phase 1 Target and Stretch goals. In order to achieve widespread market adoption, monolithic sheets of the aerogel material must be made to industry-standard sizes (8’ x 12’) at a cost that provides 5-7 year or less breakeven energy savings for consumers (<$2 sq/ft). The work performed under this Phase 1 award shows that time and materials required to make aerogel samples, which make up a significant portion of their overall cost, can be greatly reduced without sacrificing quality. AeroShield plans to use these optimized processes to produce larger, product-relevant sized aerogels in order to achieve target final material costs.

Wilke, Kyle↗

A Review of Online Monitoring within Used Nuclear Fuel Recycling Processes

The processing of used nuclear fuels and related materials is often complex and variable. The ability to quickly optimize conditions to the material being processed can aid in increasing efficiency and safety, but requires very quick determination of the conditions present in the feedstock, the process, and the product. Furthermore, accurate quantification of materials such as enriched uranium and plutonium aids in maintaining material accountancy and avoiding nuclear proliferation risks. Traditional analytical methods require process samples to be collected and analyzed in a laboratory, which often takes days to weeks. Online monitoring is suitable for collecting this information nearly instantaneously, enabling much faster optimization of the process or detection of material diversion. Online monitoring is also beneficial as it is typically based on robust and nondestructive analytical methods, so no material is removed as samples. This review examines online monitoring relevant to used nuclear fuel processing for the determination of both chemical and physical parameters. The chemical parameters include quantities such as concentration, isotopic composition, and speciation. These values are often well suited to spectroscopic or spectrometric measurements as they are fast, nondestructive, and easily implemented in an online manner. Physical quantities are often more varied and include temperature, pressure, tank fill levels, and others. Due to the specificity of these quantities, specialized instrumentation is often used. However, this instrumentation is often amendable to online monitoring.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Thermodynamic Implementations of Quantum Processes

Abstract Recent understanding of the thermodynamics of small-scale systems have enabled the characterization of the thermodynamic requirements of implementing quantum processes for fixed input states. Here, we extend these results to construct optimal universal implementations of a given process, that is, implementations that are accurate for any possible input state even after many independent and identically distributed (i.i.d.) repetitions of the process. We find that the optimal work cost rate of such an implementation is given by the thermodynamic capacity of the process, which is a single-letter and additive quantity defined as the maximal difference in relative entropy to the thermal state between the input and the output of the channel. Beyond being a thermodynamic analogue of the reverse Shannon theorem for quantum channels, our results introduce a new notion of quantum typicality and present a thermodynamic application of convex-split methods.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

TunIO: An AI-powered Framework for Optimizing HPC I/O

I/O operations are a known performance bottleneck of HPC applications. To achieve good performance, users often employ an iterative multistage tuning process to find an optimal I/O stack configuration. However, an I/O stack contains multiple layers, such as high-level I/O libraries, I/O middleware, and parallel file systems, and each layer has many parameters. These parameters and layers are entangled and influenced by each other. The tuning process is time-consuming and complex. In this work, we present TunIO, an AI-powered I/O tuning framework that implements several techniques to balance the tuning cost and performance gain, including tuning the high-impact parameters first. Furthermore, TunIO analyzes the application source code to extract its I/O kernel while retaining all statements necessary to perform I/O. It utilizes a smart selection of high-impact configuration parameters of the given tuning objective. Finally, it uses a novel Reinforcement Learning (RL)-driven early stopping mechanism to balance the cost and performance gain. Experimental results show that TunIO leads to a reduction of up to ≈73% in tuning time while achieving the same performance gain when compared to H5Tuner. It achieves a significant performance gain/cost of 208.4 MBps/min (I/O bandwidth for each minute spent in tuning) over existing approaches under our testing.

Rajesh, Neeraj↗

Develop a Fast Analysis Solver for Welding Sequence Optimization

During the shipbuilding manufacturing process, materials are exposed to significant stresses, as induced both thermally and mechanically, that alter the intended design and significantly affect the production schedule, labor hours (fitting, welding, rework, etc.), and material structural performance. The type and magnitude of deformation of a given structure depends on many factors such as the material, thickness and quality of components, the process heat input, preheat and inter-pass temperatures, type and size of welds, welding sequence and direction, location, sequence, and degree of fixturing. Numerical simulations using finite element analysis (FEA) have long been used to analyze welding-induced structural distortion. For large assemblies, transient thermal elastic-plastic analysis (TEPA) can take days or weeks to run, and optimization of welding sequence is not feasible. Simplified analysis methods were developed to reduce computational time. However, it is challenging to use these techniques to fully optimize welding sequencing because of their applied simplifications in modeling weld details. A fast analysis solver that could be used by the shipbuilding industry is being developed for optimizing welding sequences by taking full advantage of modern GPU-based HPC hardware and incorporating patented acceleration schemes. The accelerated processing factors are up to 2200 times greater for large, multi-pass welded structures.

Yang, Yu-Ping↗

MoNbTi-based Refractory Multi-principal Element Alloy System: A Review of the Thermodynamic Phase Predictions, Formed Microstructures, and Mechanical Properties as a Function of the Fabrication Methods (AM versus SPS versus VAM)

Abstract Multi-principal element alloys, particularly of refractory compositions, are an increasingly popular candidate for extreme-environment applications, including for next-generation nuclear reactors and in other industries, such as biomedical and aerospace, due to their high strength. The ability to achieve solid-solution microstructures provides these alloys with improved mechanical properties; however, these microstructures are heavily composition- and processing-dependent. In this review, the multi-principal element alloy MoNbTi-based system (with Zr, V, and Cr additions) was selected to compare the effects of elemental composition and processing route on the resulting microstructures and mechanical properties. The review provides insight into identifying the optimal alloy composition and processing route for a balance of desired microstructure and mechanical properties.

Krogh, Kara↗

Adaptive Data-Driven Deep-Learning Surrogate Model for Frontal Polymerization in Dicyclopentadiene

Frontal polymerization (FP) is a self-sustaining curing process that enables rapid and energy-efficient manufacturing of thermoset polymers and composites. Computational methods conventionally used to simulate the FP process are time-consuming, and repeating simulations are required for sensitivity analysis, uncertainty quantification, or optimization of the manufacturing process. Here, in this work, we develop an adaptive surrogate deep-learning model for FP of dicyclopentadiene (DCPD), which predicts the evolution of temperature and degree of cure orders of magnitude faster than the finite-element method (FEM). The adaptive algorithm provides a strategy to select training samples efficiently and save computational costs by reducing the redundancy of FEM-based training samples. The adaptive algorithm calculates the residual error of the FP governing equations using automatic differentiation of the deep neural network. A probability density function expressed in terms of the residual error is used to select training samples from the Sobol sequence space. The temperature and degree of cure evolution of each training sample are obtained by a 2D FEM simulation. The adaptive method is more efficient and has a better prediction accuracy than the random sampling method. With the well-trained surrogate neural network, the FP characteristics (front speed, shape, and temperature) can be extracted quickly from the predicted temperature and degree-of-cure fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Gaussian process regression for radiological contamination mapping- Applied to optimal motion planning for mobile sensor platforms [Slides]

Want to achieve best representative characterization of the entire area efficiently and accurately-Unmanned aerial/ground vehicles (UAV/UGVs) for contamination mapping. Some major challenges include: Limited battery life (move smart), Human operated (fully autonomous controls) and Many measurements (predictive mapping capabilities). The objective: Develop fully autonomous controls for mobile sensor platforms to improve efficiency and maintain performance.

61 RADIATION PROTECTION AND DOSIMETRY↗