Small-scale Model Research Articles

Lagrangian and Eulerian approach to predict the movement of radionuclides in selected potential sites in Malaysia during the monsoon period

In computing the movement of particles through the fluid, three types of dispersion models are used which are Gaussian plume models, Lagrangian puff models, and small-scale numerical models. Lagrangian particle dispersion models are increasingly used for nuclear applications. In this study, the usage of the Lagrangian model is implemented in a Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) which combines with the Eulerian model to predict the movement of radionuclides from the simulated nuclear power plant accident in selected locations in Malaysia. These include nuclear risk studies in these areas, emergency response systems, and source term analyses. This study aims to compare the risk from the dispersion of 137Cs from a simulated Nuclear Power Plant (NPP) at Mukah and Tasik Kenyir during the Northeast monsoon (8th October 2022) and Southeast monsoon (16th May 2022). The HYSPLIT model was set up based on the meteorological data during both monsoon periods and was simulated for 5 days. The outcomes revealed that the concentration of 137Cs is higher at Tasik Kenyir compared to Mukah for both during the Northeast monsoon period (3.5 x 107 Bq/m2) while for Southwest monsoon, the result is vice versa where the concentration of 137Cs at Mukah is (2.2 x 107Bq/m2). The activity concentration is then converted into rate effective dose measurement and compared with the limit suggested by ICRP which is 1 Sv per year. It is concluded that Tasik Kenyir is a more suitable location to build NPP compared to Mukah as the movement of radionuclides was dispersed to the location that contained less population.

Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?

Abstract. Snow dynamics play a critical role in the climate system, as they affect the water cycle, ecosystems, and society. In climate modelling, the representation of the amount and extent of snow on the land surface is crucial for simulating the mass and energy balance of the climate system. Here, we evaluate simulations of daily snow depths against 83 station observations in southern Germany in an elevation range of 150 to 1000 m over the time period 1987–2018. Two simulations stem from high-resolution regional climate models – the Weather Research & Forecasting (WRF) model at 1.5 km resolution and the COnsortium for Small scale MOdelling model in CLimate Mode (COSMO-CLM; abbreviated to CCLM hereafter) at 3 km resolution. Additionally, the hydrometeorological snow model Alpine MUltiscale Numerical Distributed Simulation ENgine (AMUNDSEN) is run at point scale at the locations of the climate stations, based on the atmospheric output of CCLM. To complement the comparison, the ERA5-Land dataset (9 km), a state-of-the-art reanalysis land-surface product, is also compared. All four simulations are driven by the atmospheric boundary conditions of ERA5. Due to an overestimation of the snow albedo, the WRF simulation features a cold bias of 1.2 °C, leading to the slight overestimation of the snow depth in low-lying areas, whereas the snow depth is underestimated at snow-rich stations. The number of snow days (days with a snow depth above 1 cm) is reproduced well. The WRF simulation can recreate extreme snow depths, i.e. annual maxima of the snow depth, their timings, and inter-station differences, and thereby shows the best performance of all models. The CCLM reproduces the climatic conditions with very low bias and error metrics. However, all snow-related assessments show a strong systematic underestimation, which we relate to deficiencies in the snow module of the land-surface model. When driving AMUNDSEN with the atmospheric output of the CCLM, the results show a slight tendency to overestimate snow depth and number of snow days, especially in the northern parts of the study area. Snow depth extremes are reproduced well. For ERA5-Land (ERA5L), the coarser spatial resolution leads to larger differences between the model elevation and the station elevation, which contributes to a significant correlation of climatic biases with the elevation bias. In addition, the mean snow depth and number of snow days are strongly overestimated, with conditions that are too snowy in the late winter. Extreme snow depth conditions are reproduced well in the low-lying areas, whereas strong deviations occur with more complex topography. In sum, due to the high spatial resolution of convection-permitting climate models, they show the potential to reproduce the winter climate (temperature and precipitation) in southern Germany. However, different sources of uncertainties, i.e. the spatial resolution, the snow albedo parametrisation, and other parametrisations within the snow model, prevent their further use in a straightforward manner for impact research. Hence, careful evaluation is needed before any impact-related interpretation of the simulations, such as in the context of climate change research.

YOLO-based Small-scaled Model for On-Shelf Availability in Retail

On-shelf availability (OSA) in the retail industry plays a very crucial role in continuous sales. Product unavailability may lead to a bad impression on customers and reduce sales. The retail industry may continue to develop through the rapidly advancing technology era to thrive in a market where competition is increasingly tough. Along with technological advancements in recent decades, Artificial Intelligence has begun to be applied to support OSA, particularly using object detection technology. In this research, we develop a small-scaled object detection model based on four versions of You Only Look Once (YOLO) algorithm, namely YOLOv5-nano, YOLOv6-nano, YOLOv7-tiny, and YOLOv8-nano. The developed model can be used to support automatic detection of OSA. A small-scale model has developed in the sense of post-practical implementation through low-cost mobile applications. We also use the quantization method to reduce the model size, INT8 and FP16. This small-scaled model implementation also offers implementation flexibility. With a total of 7697 milk-based retail product images and 125 different product classes, the experiment results show that the developed YOLOv8-nano model, with a mAP50 score of 0.933 and an inference time of 13.4 ms, achieved the best performance.

AGENT-BASED MODELING OF UP DILIMAN INTRACAMPUS PEDESTRIAN MOBILITY USING NETLOGO AND GIS

Abstract. In the Philippines, campus mobility studies remain underexplored. Since small-scale models help in policy making that may also be applied in a large spatiotemporal context, intracampus mobility modeling and simulation offer support for future mobility research. In this study, we designed a NetLogo model to apply ABM and GIS in understanding intracampus student pedestrian mobility. We created a deterministic model using discrete student schedule and a probabilistic model using the multinomial logistic (MNL) regression model derived from the student demographics data to analyze student movement. To provide a realistic representation of student pedestrians and campus environment, GIS spatiotemporal data was proven crucial to the pedestrian mobility models. The generated pedestrian count heatmaps show a significant increase in student count from online learning switching to face–to–face instruction. The overall campus building occupancy served as the key factor influencing the changes in agent behaviors such as walking speed and student tardiness. Moreover, the probabilistic model showed erratic movements of students. The model evaluation depends on the heavily imbalanced datasets and its multiclass nature with relatively higher model performance (F1 score > 0.8) is observed during morning, noon, and end schedule times, whereas suboptimal performance (F1 score < 0.6) where the dataset has a higher number of classes. The simulations demonstrate that ABM is a useful tool for examining pedestrian and transport patterns and can be enhanced further to allow multimodal and multiagent systems.

BiGNN: Bipartite graph neural network with attention mechanism for solving multiple traveling salesman problems in urban logistics

The multiple traveling salesman problems (MTSP), which arise from real world problems, are essential in urban logistics. Variations such as MinMax-MTSP and Bounded-MTSP aim to distribute workload evenly among salesmen and impose constraints on visited cities, respectively. Branch-and-bound (B&B) provides an exact algorithm solution for these problems. The Learn to Branch (L2B) approach guides branch node selection through deep learning. In this study, we utilize mathematical modeling of Bipartite Graph Neural Network (BiGNN) and an attention mechanism to support B&B in exploring solution spaces through imitation learning. The problems are framed to formulate mixed integer linear programming, which is different from conventional algorithms. It is proposed that a bipartite graph network approach makes a feature representation by setting a structure of constraints and variables. Experimental results showed that our model can generate more accurate solutions than three benchmark models. The BiGNN model can effectively learn the strong branch strategy, which reduces solution time by replacing complex calculations with fast approximations. Additionally, the small-scale instances model can be applied to larger-scale ones.

Evaluation of COSMO-CLM Model Parameter Sensitivity in the Study of Extreme Events across the Eastern Region of India

The present study aims to identify the parameters from the Consortium for Small-scale Modelling in CLimate Mode (COSMO-CLM) regional climate model that strongly controls the prediction of extreme events over West Bengal and the adjoining areas observed between 2013 to 2018. Metrics, namely Performance Score (PS) screen out the most persuasive parameter on model output. Additionally, the Performance Index (PI) measure the reliability of the model and Skill Score (SS) establishes the model performance against the reference simulation leading to the optimization of the model for a given variable. In this study, parameter screening for four output variables such as 2m-temperature, surface latent heat flux, precipitation and cloud cover of COSMO-CLM is accomplished. For heat wave simulations, 2m-temperature and surface latent heat flux are explored whereas cloud cover and precipitation are examined for extreme rainfall events. A total of 25 adjustable parameters representing the following parameterization schemes: turbulence, land surface process, microphysics, convection, radiation and soil. Out of the six parameterization schemes, the scaling factor of the laminar boundary layer for heat (rlam_heat) and the ratio of laminar scaling factors for heat over sea and land (rat_sea) from the land surface process is sensitive to SLH, TP. The exponent to get the effective surface area (e_surf) from the land surface has a large impact on 2m-temperature. A few parameters from microphysics (cloud ice threshold for auto conversion), convection (mean entrainment rate for shallow convection) and radiation (parameter for computing the amount of cloud cover in saturated conditions) play a significant role in producing TP, and TCC fields. It is evident from the results that the parameter sensitivities on model performance depend on the choice of the meteorological field. Furthermore, in almost all input model parameters, the model performance reveals the opposite character in different domains for a given meteorological field.

Optimization of unsteady jet control flow method for aerodynamic drag reduction of heavy truck model

The aerodynamic drag reduction of ground vehicles is crucial for meeting the increasingly strict emission standards. In this context, a potential approach to achieve this objective is to use active flow control techniques. Many active flow control techniques have been applied for ground vehicles, and the unsteady jet device has been proven to have better control energy efficiency than some other flow actuators. Therefore, this study focuses on applying this unsteady jet technique to a realistic truck model. The main parameters of the control flow device, including jet amplitude, jet angle, and control frequency were optimized to reduce the aerodynamic drag of the truck model and achieve the maximum control energy efficiency. Numerical studies were performed based on the CFD method with the k-ω SST turbulence model. A 25-level optimal Latin Hypercube design experimental study was conducted. The optimization method based on the Kriging response surface model and genetic algorithm was chosen to determine the optimal solution. The results show that the maximum energy efficiency is 9.174 % using the unsteady jet devices with the optimal parameters on the small-scale truck model.

Physical-Model-Based Assessment of Reduction of Hydraulic Forces Acting on Channel Bed through Advanced Energy Dissipator Design

The hydraulic engineering designers focus on shaping up the flow regime so that the greatest energy dissipation is ensured. On the other hand, the structural designers focus on load bearing capacity and durability of the structural components which may contradict the focus of the hydraulic experts. Such a case may occur when the spillway chute floor slab must be thin due to the limited spaces. Then, the dynamic load acting on the chute floor slab may compromise its long-term operation. The dynamic forces are created by the violent turbulences which may even coincide with the natural frequency of the reinforced concrete floor slab. Therefore, the objective of this paper is a complex approach to small-scale physical model testing which makes use of the latest rapid-prototyping techniques and the particle image velocimetry, and which in the end allows to estimate the magnitude of the dynamic hydraulic forces acting on the channel bed. The newly developed organic-shape energy dissipators play a key role in reducing the kinetic energy of water through its enhances aeration. The proposed method on a small scale can assist favorably the design process of real-scale hydraulic reinforced concrete structures.

Remove legacy perfluoroalkyl acids and emerging per- and polyfluoroalkyl ether acids by single-use and regenerable anion exchange resins: Rapid small-scale column tests and model fits

Rapid small-scale column tests (RSSCT) are used to study the removal of per- and polyfluoroalkyl substances (PFAS) for drinking water treatment by ion exchange. Breakthroughs of 15 emerging per- and perfluoroalkyl ether acids and six legacy perfluoroalkyl acid analogs are studied using a single-use PFAS-selective anion exchange resin (AER1) and a regenerable, generic anion exchange resin (AER2). The Bohart-Adams model was used to describe and predict breakthrough, with the modeled results reasonably aligned with RSSCT results in most cases, enabling shorter RSSCT duration for future applications. AER1 exhibited high uptake capacity with no breakthrough for 11 of the 21 tested PFAS during the 144,175 BV continuous operation, allowing compliance with the new National Primary Drinking Water Regulation in many application scenarios. AER2 exhibited much faster breakthroughs for most PFAS and is not a promising option for drinking water treatment. However, the summed PFAS capacity via model fit and total PFAS adsorbed via measurement were only <0.01 % of both resin capacities at full breakthrough, suggesting PFAS could only occupy a tiny portion of the ion exchange sites even for the PFAS-selective AER1. Ether group insertion in the PFAS group leads to later breakthrough, and linear isomers were better captured by the resins than the branched isomers. Overall, PFAS uptake capacity increases and kinetics decrease when the PFAS molecular volume increases. Regeneration using 10 % NaCl solutions partially released PFAS from AER2 but not from AER1, with more short-chain PFAS released than long-chain ones. Ether group insertion decreased the PFAS recoveries during the regeneration of AER2. The regenerated resins showed much faster breakthroughs than the pristine resins, making them unfavorable for drinking water treatment applications. Adsorption displacement of short-chain PFAS by long-chain PFAS was observed in pristine AER1, and post-regeneration leaching occurred for both resins, both phenomena making the resins a possible PFAS source in long-term use.

Small-scale perfusion mimic cultures in the ambr250 HT bioreactor system

Perfusion cell cultures generate higher viable cell densities (VCD) and volumetric productivity compared to fed-batch cultures. However, due to the limited availability of small-scale perfusion models, perfusion systems are seldom used to produce licensed biotherapeutics. This study evaluated two small-scale perfusion mimic protocols to bridge the research-to-production gap. Shake flasks and the ambr250 HT were used to compare centrifugation and in situ gravity settling protocols. The centrifugation protocol achieved a peak VCD >50 million cells/mL and is well-suited to media formulation and feeding strategy comparisons. The in situ gravity settling protocol achieved ∼20 million cells/mL and is well-suited to cell productivity and stability studies. The cell retention steps resulted in temporary DO and pH changes but did not affect the overall culture's health. Further, both protocols were able to recover from an imposed long-duration DO stress, although the centrifugation protocol cultures had higher cell specific oxygen consumption rates indicative of higher culture stress. Both protocols sustained the cultures for 39 days with stable cell specific productivities (∼27 pg/cell·day). Overall, this study demonstrated the feasibility of two economic small-scale perfusion mimic protocols in a standard small-scale bioreactor system, which could be translated to other multi-unit small-scale bioreactor systems.

Persona-Identified Chatbot through Small-Scale Modeling and Data Transformation

Research on chatbots aimed at facilitating more natural and engaging conversations is actively underway. With the growing recognition of the significance of personas in this context, persona-based conversational research is gaining prominence. Despite the abundance of publicly available chit-chat datasets, persona-based chat datasets remain scarce, primarily due to the higher associated costs. Consequently, we propose a methodology for transforming extensive chit-chat datasets into persona-based chat datasets. Simultaneously, we propose a model adept at effectively incorporating personas into responses, even with a constrained number of parameters. This model can discern the most relevant information from persona memory without resorting to a retrieval model. Furthermore, it makes decisions regarding whether to reference the memory, thereby enhancing the interpretability of the model’s judgments. Our CC2PC framework demonstrates superior performance in both automatic and LLM evaluations when compared to high-cost persona-based chat dataset. Additionally, experimental results on the proposed model indicate the improved persona-based response capabilities.

Modular production of small ship models using 3D printing for model tests

This paper presents a cost-effective approach for the production of small-scale ship models, particularly suitable for model testing of high-speed vessels characterized by a high Froude number. In contrast to traditional approach employed in ship model production, the proposed approach uses 3D printing technology integrated with modularity. This strategy enables the mitigation of expenses associated with procuring 3D printers, facilitates dynamic design changes, and accommodates parallel printing using multiple printers, thereby resulting in a reduction in production time. The manufacturing process involves modularization of the original CAD model, build scheduling, interface design, assembly, fabrication, and painting. A case study is presented, which demonstrates the proposed process in ship model production using 3D printing. The geometric similarity between the built model and the original CAD model was assessed by generating a point cloud using a 3D scanner. To evaluate the resistance performance of the 3D printed ship model, experiment and numerical simulation were conducted. These results indicate a high level of similarity and reliability.

Ground Effect on the Thrust Performance of Staggered Rotor System

In this study, the thrust performance of a staggered rotor system in-ground effect (IGE) and out-of-ground effect (OGE) while considering the interaction on wake characteristics were investigated experimentally. A thorough comprehension of their performance holds significant importance for trajectory planning, aircraft design, landing safety, and energy-efficient landings. The complex interactions within staggered rotor systems and the impact of ground effects make rotor distance and ground interactions critical factors influencing near-ground flight performance. The study investigated the influence and enhancements of rotor thrust performance through an examination of rotor speed, lateral distance, and altitude. Experimental tests were conducted on two small-scale rotor models to assess the effects of these parameters. These experiments compared the performance of staggered rotor systems with isolated rotors, analyzing the competition mechanism between the thrust loss caused by interference and the thrust gain of rotors IGE. Furthermore, emphasis was placed on analyzing the thrust gain issues exhibited by staggered rotor systems under the condition of H = 2R. Additionally, the analysis was focused on identifying prominent relative positions for thrust performance and parameters for improving thrust performance in ground effects in staggered rotor systems.

Toward Atomistic Understanding of Iron Phosphate Glass: A First-Principles-Based Density Functional Theory Modeling and Study of Its Physical Properties.

Iron phosphate glasses (IPGs) have been proposed as futuristic materials for nuclear waste immobilization and anode materials for lithium batteries. Recently, many attempts have been made to propose atomistic models of IPGs to explain their properties from an atomistic viewpoint. In this paper, we seek to produce small scale models of IPG that can be handled within the scheme of density functional theory (DFT) to study its electronic structure. The starting models, generated using the Monte Carlo (MC) method, were subsequently annealed using ab initio molecular dynamics (AIMD) to minimize the coordination defects. The geometry of the equilibrated structure was then optimized at 0 K. This hybrid approach (MC + AIMD + DFT optimization) produced good atomistic models of IPG, which can reproduce the experimentally observed band gap, vibrational density of states, magnetic moment of Fe, and mechanical and optical properties. Computationally expensive melt-quench simulation can be avoided using the present approach, allowing the use of DFT for accurate calculations of properties.

Effects of Different CaO/Al2O3 Ratios on the Phase Composition and Desulfurization Ability of CaO-Based Desulfurizers in Hot Metal

In response to the development of low-carbon smelting technology, reducing the use of fluor-containing materials in desulfurizers is an important research topic. The development of new-generation KR (Kambara Reactor) desulfurizers is shifting towards a higher Al2O3 content rather than CaF2, yet there is currently an absence of thorough and comprehensive mechanisms for desulfurization. Consequently, this research provides an extensive comparison using a specially constructed small-scale KR desulfurization hot model test, alongside FactSage simulation and SEM analysis (of desulfurization process). The findings indicate that at 1400 °C, for the desulfurization of molten iron, the capacity for desulfurization initially increases and then diminishes as the Al2O3 content in the KR desulfurizer rises. With Al2O3 content in the desulfurizer below 22 wt.%, the phase composition predominantly consists of C3A, employing a solid(slag)–liquid(metal) diffusion method for desulfurization. The optimal desulfurization capacity (Ls: 64.1) is observed when the Al2O3 content is 15 wt.%, attributed to the simultaneous presence of CaO particle precipitation and C3A. However, as the Al2O3 content reaches 20 wt.%, all the oversaturated CaO integrates into C3A, leading to a reduction in Ls from 64.1 to 10.7, thereby diminishing the desulfurization capacity by approximately sixfold. When Al2O3 exceeds 22 wt.%, the phase composition transitions from the C3A to C12A7 phase, and the desulfurization approach shifts from solid(slag)–liquid(metal) to liquid(slag)–liquid(metal) diffusion, with Ls decreasing to 23.4. This reduction is due to C12A7’s lower sulfur capacity compared to C3A and the absence of saturated CaO particle precipitation. Therefore, for Al2O3 to effectively replace fluorite in KR desulfurizers, a higher presence of C3A phases and CaO particle precipitation are essential. The desulfurizer must contain over 65 wt.% CaO and maintain Al2O3 levels at 10~16.2 wt.%.

Development of a high-throughput scale-down model in Ambr® 250 HT for plasmid DNA fermentation processes.

Recent advances in messenger ribonucleic acid (mRNA) vaccines and gene therapy vectors have increased the need for rapid plasmid DNA (pDNA) screening and production within the biopharmaceutical industry. High-throughput (HT) fermentor systems, such as the Ambr® 250 HT, can significantly accelerate process development timelines of pDNA upstream processes compared to traditional bench-scale glass fermentors or small-scale steam-in-place (SIP) fermentors. However, such scale-down models must be qualified to ensure that they are representative of the larger scale process similar to traditional small-scale models. In the current study, we developed a representative scale-down model of a Biostat® D-DCU 30 L pDNA fermentation process in Ambr® 250 HT fermentors using three cell lines producing three different constructs. The Ambr scale-down model provided comparable process performance and pDNA quality as the 30 L SIP fermentation process. In addition, we demonstrated the predictive value of the Ambr model by two-way qualification, first by accurately reproducing the prior trends observed in a 30 L process, followed by predicting new process trends that were then successfully reproduced in the 30 L process. The representative and predictive scale-down Ambr model developed in this study would enable a faster and more efficient approach to strain/clone/host-cell screening, pDNA process development and characterization studies, process scale-up studies, and manufacturing support.

Comparison of COSMO and ICON-LAM high-resolution numerical forecast for Romanian territory: Case studies and evaluation

This paper aims to offer the first detailed inter-comparison of the performance from the numerical weather prediction models Consortium for Small-scale Modeling (COSMO) and Icosahedral Nonhydrostatic General Circulation Model, Limited Area Mode (ICON-LAM), integrated for the Romanian territory at the horizontal resolution of 2.8 km. As the ICON numerical model is set to replace COSMO, a comparison is carried out between the operational configurations of the two models. First, two cases with severe weather (strong atmospheric instability, observed heavy precipitation, and strong wind) are analyzed: February 3-6, 2020 (heavy precipitation [snow] and very strong wind) and May 3-5, 2020 (heavy precipitation). This is followed by a statistical inter-comparison between the two models for the summer of 2020 (June, July, August). A statistical evaluation of the forecast quality from the two models is performed objectively, through statistical scores computed using surface observations from all available meteorological stations on the Romanian territory. Stratifications depending on station altitude and location are carried out, with emphasis on stations of particular interest, depending on the evolution of the synoptic situation. Following the statistical evaluation, results for the precipitation forecast do not show significant improvement in favor of either model. However, ICON-2.8 km mostly outperforms COSMO-2.8 km for surface parameters.

Experimental Study on the Wind Erosion Resistance of Aeolian Sand Solidified by Microbially Induced Calcite Precipitation (MICP).

Microbially induced calcite precipitation (MICP) is an emerging solidification method characterized by high economic efficiency, environmental friendliness, and durability. This study validated the reliability of the MICP sand solidification method by conducting a small-scale wind tunnel model test using aeolian sand solidified by MICP and analyzing the effects of wind velocity (7 m/s, 10 m/s, and 13 m/s), deflation angle (0°, 15°, 30°, and 45°), wind erosion cycle (1, 3, and 5), and other related factors on the mass loss rate of solidified aeolian sand. The microstructure of aeolian sand was constructed by performing mesoscopic and microscopic testing based on X-ray diffraction analysis (XRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). According to the test results, the mass loss rate of solidified aeolian sand gradually increases with the increase in wind velocity, deflation angle, and wind erosion cycle. When the wind velocity was 13 m/s, the mass loss rate of the aeolian sand was only 63.6%, indicating that aeolian sand has excellent wind erosion resistance. CaCO3 crystals generated by MICP were mostly distributed on sand particle surfaces, in sand particle pores, and between sand particles to realize the covering, filling, and cementing effects.

Effects of Staggered Plates on the Uplift Failure State and Bearing Capacity of NT-CEP Pile Groups

Plate position is an important parameter of a New Type Concrete Expanded-Plate (NT-CEP) pile. In this study, two small-scale test models and two, four, six, and nine finite element models were established using a visual small-scale model of half-section piles, an undisturbed soil pull-out test, and the ANSYS finite element software R19.0. The NT-CEP pile groups were studied with the plates set at staggered positions. This study mainly analyzes the displacement contours, stress curves, and load-displacement curves under the action of vertical tension and determines the influence of staggered plate positions on the performance and bearing capacity of the NT-CEP pile group, which provides theoretical support for its application in practical engineering. The bearing plate positions affect the performance of the pile group. The stress distribution in each pile in the pile group is uneven when plate positions are staggered, and the pile with the lower plate position bears a greater force. This has a great influence on the bearing capacity of the NT-CEP pile group. If allowed, the plates can be first set at the same position. If appropriate, the plates can be set in staggered positions; however, a reasonable distance between the upper and lower plates should be considered.

Targeted optimization in small-scale atomic structure calculations: application to Au I

The lack of reliable atomic data can be a severe limitation in astrophysical modelling, in particular of events such as kilonovae that require information on all neutron-capture elements across a wide range of ionization stages. Notably, the presence of non-orthonormalities between electron orbitals representing configurations that are close in energy can introduce significant inaccuracies in computed energies and transition probabilities. Here, we propose an explicit targeted optimization (TO) method that can effectively circumvent this concern while retaining an orthonormal orbital basis set. We illustrate this method within the framework of small-scale atomic structure models of Au I, using the Grasp2018 multiconfigurational Dirac–Hartree–Fock atomic structure code. By comparing to conventional optimization schemes we show how a TO approach improves the energy level positioning and ordering. TO also leads to better agreement with experimental data for the strongest E1 transitions. This illustrates how small-scale models can be significantly improved with minor computational costs if orbital non-orthonormalities are considered carefully. These results should prove useful to multi-element atomic structure calculations in, for example, astrophysical opacity applications involving neutron-capture elements.