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From digital engineering tools to embedded structural sensors, the use of modern tools to democratize the data and sensor fusion to inform health and maintenance decisions for the reactor and facility
Modeling Successes Dounreay – Site Closure Optimization and Risk Reduction Re-structuring and streamlining a complex project plan to achieve site decommissioning in reduced time and at reduced cost, through automated task prioritization. Idaho AMWTP – Multi-stream Optimization Speeding up a complex facility with many process lines, by utilizing all of the process lines more efficiently and reducing overall plant downtime, by coordinating repairs and shutdowns. AWE –Waste Repackaging and Relocation Relocating schedule for waste material from an old storage facility to a new purpose-built one, safely, and in a timely fashion that removed the need to reassure the safety of the old facility. Sellafield – Effluent, Sludge and HAW Treatment Improving a wide range of chemical engineered processes including aqueous effluent treatment, chemically-reactive sludge management and waste vitrification processing. Strategic Petroleum Reserve – Cavern Management Linking chemistry models of petroleum prediction, through engineering cavern management to financial modeling of gasoline sales to benefit US government
Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods and tools designed to optimize plant operations (e.g., maintenance/replacement schedules, optimal maintenance postures for plant structures, systems, and components [SSCs]) in a manner that is more cost effective than current approaches and makes better use of available SSC health data. The Risk-Informed Asset Management (RIAM) project targets this research area by creating a direct bridge between component equipment reliability (ER) data and system engineer decision making regarding maintenance activity scheduling and component aging management. In this respect, one challenge that NPP system engineers are facing is that the amount of ER data being continuously generated is not only extremely large in size, but it comes in different forms: textual (e.g., condition or maintenance reports) and numeric (e.g., generated by monitoring systems). All these data elements provide them with valuable insights and information regarding: 1) the discovery of anomalous behaviors or degradation trends, 2) the identification of the possible causes behind such behaviors/trends, and 3) the prediction of their direct consequences. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers/databases), others are conceptual in nature: data elements come in different formats (e.g., numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). The activities performed by the RIAM project during FY23 directly tackles the need to simultaneously integrate the analysis of ER data in all its forms, numeric and textual. Note that such task has never been performed before due to the complexity of the systems under consideration but, most importantly, because of the technical challenges behind the harmonization of ER data formats and the lack of adequate computational methods to analyze them. Our approach borrows ideas and concepts from the medical field where integration of several data sources is vital to assist medical practitioners to perform correct diagnosis and indicate optimal treatments. In our view a NPP asset is equivalent to a patient in a medical context. The main difference is the complexity of a human body is a magnitude more complex when compared to typical assets commonly present in NPPs (e.g., centrifugal pumps, or motor operated valves). This simplifies our first requirement when analyzing heterogenous ER data formats: to put data into “context”. Context is here intended as the additional piece of information that is needed by ER data analysis tools to understand what these data elements are referring to, i.e., which king of knowledge they are generating. In our context, this knowledge can be translated into models that capture the form and functional architecture of assets/systems, their dependencies, and how they interact. These models actually emulate the knowledge that that NPP system engineers possess about assets and systems; this is their key of success when analyzing ER data, their challenge is ability to handle large amount of data. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional, i.e. cause-effect, relations. Then, ER data elements are processed by identifying first of all which elements of the developed MBSE elements they are referring to. For numeric ER data this task is fairly easy since it is possible to precisely pinpoint what MBSE elements the corresponding sensor are observing (e.g., bearing temperature of a centrifugal pump). Task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process as “knowledge extraction”. Once again, we borrow the experience in the medical field where methods to extract knowledge from textual data have been developed in the past decade. The missing element for us is the availability of a complete dictionary of NPP related concepts (in addition to the MBSE models presented earlier) that can put “text into context”. In FY23, such dictionary has been developed along with all the computational elements required for knowledge extraction. Lastly, once numeric and textual ER data elements have been processed and “understood”, then the last step is the discovery of possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if the
The urgent need for low latency, high-compute and low power on-board intelligence in autonomous systems, cyber-physical systems, robotics, edge computing, evolvable computing, and complex data science calls for determining the optimal amount and type of specialized hardware together with reconfigurability capabilities. With these goals in mind, we propose a novel comprehensive graph analytics based high level synthesis (GAHLS) framework that efficiently analyzes complex high level programs through a combined compiler-based approach and graph theoretic optimization and synthesizes them into message passing domain-specific accelerators. This GAHLS framework first constructs a compiler-assisted dependency graph (CaDG) from low level virtual machine (LLVM) intermediate representation (IR) of high level programs and converts it into a hardware friendly description representation. Next, the GAHLS framework performs a memory design space exploration while account for the identified computational properties from the CaDG and optimizing the system performance for higher bandwidth. The GAHLS framework also performs a robust optimization to identify the CaDG subgraphs with similar computational structures and aggregate them into intelligent processing clusters in order to optimize the usage of underlying hardware resources. Finally, the GAHLS framework synthesizes this compressed specialized CaDG into processing elements while optimizing the system performance and area metrics. Evaluations of the GAHLS framework on several real-life applications (e.g., deep learning, brain machine interfaces) demonstrate that it provides 14.27× performance improvements compared to state-of-the-art approaches such as LegUp 6.2.
This paper presents a design and optimization methodology for a 200-kW medium-frequency transformer (MFT) based on low-loss magnetic core (FINEMET FT-3TL). The proposed optimal design methodology consists of predesign, preliminary design, and optimal design. In the preliminary design, the parallel-concentric winding structure is selected to increase the current carrying capability and reduce the leakage inductance. Based on the parallel-concentric winding concept, a novel cooing and insulation structure with 3-D printed bobbins are proposed. The optimal designed MFT prototype achieves a power density higher than 19.23 kW/L. The electrical insulation system is tested at 12 kV ac peak voltage. In addition, the partial discharge (PD) test is conducted at 7.5 kV ac peak voltage to ensure the PD-free design. The high-frequency bipolar pulsewidth modulation voltage insulation (PD) test is the first time applied in MFT design and test process. Finally, the transformer is applied in a dual-active-bridge (DAB) converter with 200 kW rated power. The peak efficiency of the DAB converter is 99.53%, and the efficiency at 200 kW is 98.85%. The peak efficiency of MFT is 99.844%, and the efficiency at 200 kW is 99.842%.
develop an inexpensive accelerating structure with a high gradient and high shunt impedance. Euclid Techlabs proposes to optimize this technology (based on a successful accelerating structure with distributed coupling developed at SLAC) for industrial applications and serial production. Future colliders require high gradient and high efficiency accelerating structures (high shunt impedance). The SLAC accelerator laboratory recently developed a series of accelerating structures with distributed coupling. Such a structure includes one or more parallel waveguides which are loaded by accelerating cavities. This approach allows configurations where no RF power is flowing axially through the accelerating cavity while maintaining a traveling RF wave through the cross-section of the accelerating structure. In this case the accelerating cavities are isolated at the beam pipe. Conventional traveling wave structures typically couple the RF power through the beam pipe. This feature constrains the cavity shunt impedance to relatively small values. For this reason, the distributed coupling design improves efficiency while reducing cost and improving the operational flexibility of particle accelerators. High gradient operation poses the risk of having a high breakdown rates. In traditional standing and traveling wave structures, RF power flows through the accelerating cells. For distributed coupling structures, this risk is reduced because the absence of power flow through the accelerating cavity allows configurations where no power flows through the beam pipe. Finally, the distributed coupling accelerating structure design allows fabricating structures in two halves. This significantly reduces the complexity of machining, brazing and tuning.
Addressing the urgent need to develop novel drugs against drug-resistant Mycobacterium tuberculosis (M. tb) strains, ecumicin (ECU) and rufomycin I (RUFI) are being explored as promising new leads targeting cellular proteostasis via the caseinolytic protein ClpC1. Details of the binding topology and chemical mode of (inter)action of these cyclopeptides help drive further development of novel potency-optimized entities as tuberculosis drugs. ClpC1 M. tb protein constructs with mutations driving resistance to ECU and RUFI show reduced binding affinity by surface plasmon resonance (SPR). Despite certain structural similarities, ECU and RUFI resistant mutation sites did not overlap in their SPR binding patterns. SPR competition experiments show ECU prevents RUFI binding, whereas RUFI partially inhibits ECU binding. The X-ray structure of the ClpC1-NTD-RUFI complex reveals distinct differences compared to the previously reported ClpC1-NTD-cyclomarin A structure. Surprisingly, the complex structure revealed that the epoxide moiety of RUFI opened and covalently bound to ClpC1-NTD via the sulfur atom of Met1. Furthermore, RUFI analogues indicate that the epoxy group of RUFI is critical for binding and bactericidal activity. Overall, the outcomes demonstrate the significance of ClpC1 as a novel target and the importance of SAR analysis of identified macrocyclic peptides for drug discovery.
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Highlights: • S31W and R145W introduce an extra fluorophore in βB2-crystallin. • S31W and R145W destabilize βB2-crystallin and promote thermal aggregation. • S31W accelerates but R145W retards UV-induced aggregation of βB2-crystallin. • The number of Trp fluorophores in β/γ-crystallin is evolutionarily optimized. β/γ-Crystallins are predominant structural proteins in vertebrate lens with unique properties of extremely high solubility, long-term stability and resistance to UV damage. Four conserved Trp residues in β/γ-crystallins account for UV absorbance and thereafter fluorescence quenching to avoid photodamage. Herein we found that βB2-crystallin Trp fluorescence was greatly enhanced by the introduction of an extra unquenched Trp fluorophore by cataract-associated mutations S31W and R145W. Both mutations impaired oligomerization, decreased stability and promote thermal aggregation, while S31W was more deleterious. S31W accelerated βB2-crystallin aggregation under UV damaging conditions, whereas R145W delayed. These observations suggested that the introduction of an extra Trp fluorophore had complicated effects on βB2-crystallin stability and aggregation against various stresses. Our findings highlight that the number of Trp fluorophores in β/γ-crystallin is evolutionarily optimized to exquisitely perform their structural roles in the lens.
The directed self‐assembly of nanoscale materials into ordered superlattices presents a powerful strategy for creating next‐generation materials with programmable mechanical, optical, and photonic properties. Deoxyribonucleic acid (DNA) origami has emerged as a versatile scaffold for encoding nanoscale geometry and guiding the crystallization of complex 3D architectures. However, a systematic understanding of the parameters that govern the efficiency and quality of superlattice formation remains limited. In this study, we utilize octahedral DNA nanoscale frames as a model system to investigate the relative influence of key factors, including buffer composition, ionic strength, frame concentration, and thermal annealing protocols, on the size, order, and reproducibility of the resulting superlattices. Our findings provide a quantitative framework to rationally optimize DNA‐based assembly pathways. Structural characterization via small‐angle x‐ray scattering (SAXS), scanning electron microscopy (SEM), and optical microscopy validates the quality and fidelity of the assembled lattices. Moreover, by templating these DNA frameworks into inorganic replicas, we establish general design principles that extend beyond biomolecular systems, providing a foundation for the synthesis of programmable materials in broader nanofabrication contexts.
This study explores the performance and stability of ammonium and phosphonium-based polymeric ionic liquids (PILs) with methyl and butyl substituents in moisture-swing direct air capture of CO 2 . The polymers are synthesized with chloride counterions, followed by ion exchange to the bicarbonate ion, and tests for CO 2 capture capacity and stability under cyclic wet–dry conditions. The phosphonium polymer with methyl substituents [PVBT-MeP] demonstrates the highest CO 2 capture capacity at ≈510 µmol g⁻¹, attributed to minimal steric hindrance and stronger ion pairing with bicarbonate. However, oxidative degradation is detected by 31 P NMR spectroscopy after the moisture swing experiment, with the appearance of a phosphine oxide peak at 61.28 ppm, which indicates phosphorus oxidation as the primary degradation pathway. In contrast, the ammonium polymer with butyl substituents [PVBT-BuN] exhibits the highest stability, showing no degradation over five moisture swing cycles. Additional stability experiments in 0.5 m KHCO 3 solutions reveal no degradation for any PIL, suggesting that oxidative degradation is driven by dynamic acid-base reactions during the moisture swing cycles in the air. Furthermore, these findings reveal the potential of phosphonium-based PILs for moisture-swing direct air capture, achieving high capacity while highlighting the need for optimized stability through counterion and structural design.
Porting a complex scientific code, such as the E3SM land model (ELM), onto a new computing architecture is challenging. The paper presents design strategies and technical approaches to develop an ELM ecosystem dynamics model with compiler directives (OpenACC) on NVIDIA GPUs. The code has been refactored with advanced OpenACC features (such as deepcopy and routine directives) to reduce memory consumption and to increase the levels of parallelism through parallel loop reconstruction and new data structures. As a result, the optimized parallel implementation achieved more than a 140-time speedup (50 ms vs 7600 ms), compared to a naive implementation that uses OpenACC routine directive and parallelizes the code across existing loops on a single NVIDIA V100. On a fully loaded computing node with 44 CPUs and 6 GPUs, the code achieved over a 3.0-times speedup, compared to the original code on the CPU. Furthermore, the memory footprint of the optimized parallel implementation is 300 MB, which is around 15% of the 2.15 GB of memory consumed by a naive implementation. This study is the first effort to develop the ELM component on GPUs efficiently to support ultra-high-resolution land simulations at continental scales.
Here, this study investigates the potential advantages of lattice structure-based bound metal material extrusion (MEX) 3D printing for fabricating high-performance copper heat sinks. Copper powder-filled polymer filaments, with a copper content of > 90 wt.%, were developed specifically for the bound metal MEX 3D printing process. Three types of structures—planar, strut, and surface lattices—were 3D printed to facilitate efficient heat transfer pathways within the heat sinks. Subsequent post-processing steps, including polymer removal and sintering, were performed to achieve dense copper parts. Hot isostatic pressing was further employed to enhance the sintered density from 93 to 98%. Finite element analysis (FEA) simulations were conducted to assess the heat transfer efficiency of the designs, and heat transfer experiments were performed using a custom setup to validate the simulation results. Additionally, this research explores the use of extended hold times during pre-sintering and a reduced atmosphere to enhance the %IACS values (electrical conductivity) and thermal performance of the bound metal MEX 3D printed copper heat sinks. The investigation combines experimental analysis, including simulations and heat transfer experiments, to gain insights into the structure-material property relationships and optimize the thermal performance of the printed heat sinks.
Refractory multi-principal element alloys (RMPEAs) hold significant potential for advancing ultra-high-temperature technologies due to their remarkable strength retention under these conditions. However, the beneficial effects of alloying on microstructural evolution and mechanical properties are often obscured by the presence of interstitial alloying elements. The intricate interactions between substitutional alloying elements of Groups IV, V, and VI and interstitial content, whether intentional or incidental, remain poorly understood. In this study, we investigate the impact of dissolved oxygen on the microstructural evolution and stability of RMPEAs primarily composed of Group V elements, Nb and V, along with Group IV elements Ti, Zr, and Hf. We show that the strong affinity of oxygen for Group IV elements Zr and Hf leads to the internal precipitation of oxides during heat treatment, which competes with the homogenization of the as-cast dendritic microstructure. Here, we demonstrate that careful control and design of oxygen content and Group IV alloying additions are essential to minimize micro-segregation and prevent the detrimental precipitation of internal oxides. To address these challenges, we propose employing non-equilibrium solidification calculations to predict and optimize as-cast solidification structures, thereby reducing the reliance on homogenization processes.
To solve the failure problem of hot stamping tool, the CR7V steel was deep cryogenically treated at different dwelling times with multiple tempering. The hardness, impact toughness and wear resistance of the CR7V steel were examined, and the effects of deep cryogenic treatment on the microstructure of the CR7V steel were studied by means of optical microscope, scanning electron microscope and energy-dispersive spectroscopy. The results show that deep cryogenic treatment has little effect on the hardness of the tempered CR7V steel, but improves its impact toughness and wear resistance. The improvement in mechanical properties is a result of the combination of dissolution of large carbide particles, formation of small-sized and uniformly distributed carbide particles and fine tempered martensite structure. The cryogenic treatment is optimized as follows: vacuum gas quenching, deep cryogenic treatment at − 196 °C for 6 h and triple tempering at 560 °C for 2 h, which improves the wear resistance by 68% and the impact toughness by 58% compared to the conventional heat treatment.
The morphology of heterogeneous catalysts can impact their performance. However, standard manufacturing methods like extrusion or pelleting offer little options for tailoring catalyst shape. Herein, stereolithographic 3D printing is used to produce catalysts with controlled topologies to enhance their performance. A series of magnetic stir-bar compartments (SBC) were 3D printed and tested as catalysts for sucrose hydrolysis. The SBC were printed using acrylic acid (AA) and 1,6-hexanediol diacrylate (HDDA) as acid sites and hydrophobic crosslinking domains, respectively. Variations in the number and tilt direction of the SBC blades produced significant changes in their apparent catalytic activities. These changes resulted from differences in the fraction of active surface effectively interacting with the reactants in solution, as revealed by computational fluid dynamics simulations. Moreover, varying HDDA:AA ratios in SBC regulated reactant-surface interactions to control catalytic activity. Overall, 3D printing catalysts enables quick performance optimization by simultaneously controlling macroscopic structure and molecular composition.
Microwave-assisted ammonia synthesis is a promising alternative to the energy-intensive Haber-Bosch process, specially at small- and medium-scale with renewable H 2 as resource. Here, we report that Cs promoted Ru/CeO 2 catalyst exhibits considerable activity at 533 K and ambient pressure. In this work, the combined theoretical and experimental approaches are adopted to optimize the electronic and geometric structures of Ru on the catalysts. Both DFT modeling work and structural characterization show that the strong interaction between Ru and CeO 2 results in the formation of highly dispersed Ru particles favoring ammonia synthesis. The higher electron donating ability of CeO 2 and lower electronegativity of Cs promoter result in higher electron density on Ru reducing the N≡N dissociation barrier. Finally, the work demonstrates the potential of microwave-assisted catalytic process in activating stable molecules for ammonia synthesis.
Pre-existing second phase particles can induce particle stimulated nucleation (PSN) and pinning effect on grain boundaries during DRX process of magnesium alloys. The interaction among pinning effect, PSN mechanism and strain-induced grain boundary migration (SIBM) nucleation mechanism imposes challenge in quantitative study of microstructure evolution. A phase field model that describes discontinuous dynamic recrystallization process considering second phase particles (PF-DDRX-SP) which mainly distribute along grain boundaries is developed. The second phase order parameter with diffusion interface is introduced to the free energy function. With considering the pinning effect of second phase particles on grain boundaries, the grain boundary energy couples the interaction between second phase and grains. The effect of second phase on dislocation density evolution is considered in the model, and the PSN and SIBM mechanism are both executed by limiting the nucleation position to the grain boundary and the second phase interface. For the validation, the initial topology consistent with initial microstructure is applied to the simulation of DRX kinetics and flow behavior of AZ80 magnesium alloy, and great reliability and accuracy of developed model is indicated. Furthermore, through the model, the microstructure evolution with different particle size and with different volume fraction of round particle during DRX process were simulated. Finally, the results show that particles with higher dispersion reduce the number of DRX nuclei, which indicate that second phase particles suppress the nucleation at grain boundaries because of the pinning effect. More importantly, the proposed model could also quantitatively predict the relationships between the parameters of second phase particles and DRX behaviors, and enable to optimize the initial second phase structure in a uniform grain structure during thermomechanical process.