Computer Code Research Articles

A proposed numerical method for absolute efficiency calibration of α-spectrometers and its application for activity calculation

In this work, an absolute method to calibrate an α-spectrometer is proposed taking into account the Source-to-Detector, and lateral distances due to eccentric source distribution. An analytical formula to calibrate an α-spectrometer is derived and the Simpson's integration method was utilized to solve these equations in its integral form numerically using a written C computer code. The general Monte Carlo N-particle code, MCNP as well as experimental measurements for some standard α-emitters are used to benchmark the proposed method. An agreement was found between the efficiency results calculated by MC and the proposed method with a maximum relative difference of about 0.5%. While, experimental measurement of α-emitters activity employing the proposed method differs by about 1.65% from the certified values. Accounting for the man made error allows to accurately quantify the assayed sample. Therefore, the inaccuracy in the efficiency results due to non-accurate inputs pertained to the source, and detector radii, Source–Detector distances, and eccentric source distribution are investigated in the Source-to-Detector distance range of (4 to 44 mm). The results show that a difference of ±1% in the detector radius, and Source-to-Detector distance than the normal values yields a relative difference of about ±2%, while a difference of ±50% in the source radius or source lateral distance from detector symmetry axis could only yields inaccuracy of less than ±3% in the efficiency results.

Retraction: Sanal Flow Choking: A Paradigm Shift in Computational Fluid Dynamics Code Verification and Diagnosing Detonation and Hemorrhage in Real-World Fluid-Flow Systems.

[This retracts the article DOI: 10.1002/gch2.202000012.].

Multi-scaled temporal modeling of cardiovascular disease progression: An illustration of proximal arteries in pulmonary hypertension

The progression of cardiovascular disease is intricately influenced by a complex interplay between physiological pathways, biochemical processes, and physical mechanisms. This study aimed to develop an in-silico physics-based approach to comprehensively model the multifaceted vascular pathophysiological adaptations. This approach focused on capturing the progression of proximal pulmonary arterial hypertension, which is significantly associated with the irreversible degradation of arterial walls and compensatory stress-induced growth and remodeling. This study incorporated critical characteristics related to the distinct time scales for the deformation, thus reflecting the impact of mean pressure on artery growth and tissue damage. The in-silico simulation of the progression of pulmonary hypertension was realized based on computational code combined with the finite element method (FEM) for the simulation of disease progression. The parametric studies further explored the consequences of these irreversible processes. This computational modeling approach may advance our understanding of pulmonary hypertension and its progression.

Computational characterisation of the heat flow meter method applied to moist bio-based insulating building materials

This paper investigates the role of moisture content on the measurement of the thermal conductivity of wood-based low-density fibreboards (LDFs) used for building insulation. To that purpose, a computational code of coupled transfers was used to simulate the widely used Heat Flow Meter (HFM) method. To widen the range of study, the thermal conductivity is obtained with a mixing law model to account for the properties and fractions of the constitutive phases. This mix law model was fitted from thermal conductivity values reported for commercial LDFs. The prediction of the heat flow density during the HFM test was compared with the measurements for dry and wet LDF. Through a series of case studies, the simulations showed that, in the case of moist products, the thermal conductivity determined by the HFM method was overestimated by up to 20%. This overestimate depends on the density distribution, thickness, moisture content, and measurement parameters such as time and the equilibrium criterion. Recommendations are made to limit this error.

Impurity sensitivity analysis for activated structure materials of OPR-1000

Various kinds of radioactive wastes are expected to be generated resulting from the decommissioning of nuclear power plants. For the safe management of decommissioning wastes, the preliminary classification of radioactive wastes such as very low level, low level, intermediate level, and regulatory clearance has to be performed. In this regard, we intend to conduct an activation analysis and radioactive waste level estimation using a computational simulation code for the structure materials of an OPR-1000, a standard nuclear power plant in South Korea. In addition, the structure material that constitutes the reactor vessel, reactor vessel internals, and bio-shield concrete of the nuclear power plant contains small amounts of unintended impurities. The results of the activation analysis may vary depending on the presence of those impurities. In this study, a detail modeling of nuclear reactor and the activation analysis are carried out combined the MCNP code with the ORIGEN-S code. Based on this methodology, the sensitivity test is done for impurities of structure materials. From the result of the specific activity of activated structure material, the cobalt impurity is found to be the most important contributor in the level determination of radioactive wastes. However, the bio-shield concrete has the highest specific activity when Li impurity is contained. From this study, some impurities affect radioactive waste level, thus it is recommended to consider structure material impurities for decommissioning the nuclear power plants.

Nonlinear Geometrical Effect on Deflection Characteristics of Fiber/Metal Laminates

The deflection behavior of the fiber-metal laminated structures is envisaged in this research using higher-order deformation kinematics and large (finite) deformations of geometries via Green–Lagrange strain. The panel governing equation is derived using variational principle and solved numerically considering the nonlinear finite element (FE) steps. The necessary responses are obtained using in-house MATLAB computer code and compared with an in-house simulation model (prepared in ANSYS) and experimental data (using in-house experimental set-up). The results are verified and agree with the published data available in the open domain. The proposed model results show better consequences compared to the simulation results. The detailed responses are computed for the variable geometrical conditions (i.e. aspect ratio, end support, side-to-thickness ratio), loading and stacking sequences, including the metal layer placing. The inferences indicate the importance of nonlinearity in the framework of a higher-order kinematic model for analysing the metal laminated structure.

MELCOR – DAKOTA coupling for uncertainty analyses in the SNAP environment/architecture

Uncertainty estimation to assess figures of merit characterizing evolution of a severe accident transient is a topic of current investigation in development of best-estimate plus uncertainty methodology. The probabilistic method to propagate input uncertainty is one of the methodologies used to develop Uncertainty Analyses (UAs). Using this methodology, UAs are performed by sampling probability distributions that describe the range of possible values that computer simulation model inputs can have. For each sample (or realization) of a set of uncertain input parameters, a computer simulation is performed. From the range of code simulation results obtained for each input realization, a distribution of code results is obtained. In this process, the distribution of input uncertainties is propagated to obtain a distribution of possible code results (i.e., the code output uncertainty). This probabilistic methodology is facilitated using Uncertainty Tools (UTs), which can be coupled with the accident analysis computer code to perform an UA. One of the UTs currently available is DAKOTA, developed by Sandia National Laboratories. DAKOTA is also provided as a SNAP plug-in. SNAP is a graphical user interface designed to support the use of USNRC codes (MELCOR, TRACE, etc). This paper is entirely derived from the NUREG/IA-532 issued by USNRC in April 2023 (Mascari et al., 2023) and has as a major target to summarize the main needs of UA in severe accident, the main element of the probabilistic method to propagate input uncertainty, and the workflow within SNAP to assist other interested analysts with their applications given they are members of the USNRC Cooperative Severe Accident Research Program (CSARP).

Preserving Lagrangian structure in data-driven reduced-order modeling of large-scale dynamical systems

This work presents a nonintrusive physics-preserving method to learn reduced-order models (ROMs) of Lagrangian systems, which includes nonlinear wave equations. Existing intrusive projection-based model reduction approaches construct structure-preserving Lagrangian ROMs by projecting the Euler–Lagrange equations of the full-order model (FOM) onto a linear subspace. This Galerkin projection step requires complete knowledge about the Lagrangian operators in the FOM and full access to manipulate the computer code. In contrast, the proposed Lagrangian operator inference approach embeds the mechanics into the operator inference framework to develop a data-driven model reduction method that preserves the underlying Lagrangian structure. The proposed approach exploits knowledge of the governing equations (but not their discretization) to define the form and parametrization of a Lagrangian ROM which can then be learned from projected snapshot data. The method does not require access to FOM operators or computer code. The numerical results demonstrate Lagrangian operator inference on an Euler–Bernoulli beam model, the sine-Gordon (nonlinear) wave equation, and a large-scale discretization of a soft robot fishtail with 779,232 degrees of freedom. The learned Lagrangian ROMs generalize well, as they can accurately predict the physical solutions both far outside the training time interval, as well as for unseen initial conditions.

Optimal 3D angular sampling with applications to cryo-EM problems

The goal of cryo-EM experiments in the biological sciences is to determine the atomic structure of a molecule and deduce insights into its functions and mechanisms. Despite improvements in instrumentation for data collection and new software algorithms, in most cases, individual atoms are not resolved. Model building of proteins, nucleic acids, or molecules in general, is feasible from the experimentally determined density maps at resolutions up to the range of 3–4 Angstroms. For lower-resolution maps or parts of maps, fitting smaller structures obtained by modelling or experimental techniques with higher resolution is a way to resolve the issue. In practice, we have an atomic structure, generate its density map at a given resolution, and translate/rotate the map within a region of interest in the experimental map, computing a measure-of-fit score with the corresponding areas of the experimental map. This procedure is computationally intensive since we work in 6D space. An optimal ordered list of rotations will reduce the angular error and help to find the best-fitting positions faster for a coarse global search or a local refinement. It can be used for adaptive approaches to stop fitting algorithms earlier once the desired accuracy has been achieved. We demonstrate how the performance of some fitting algorithms can be improved by grouping sets of rotations. We present an approach to generate more efficient 3D angular sampling, and provide the computer code to generate lists of optimal orientations for single and grouped rotations and the lists themselves.

The Advent of Virtual Digital Assets: Analysing its Status in the Fintech Sector

Fintech, or financial technology, introduces the role of technology in finance, which forwards the discussion on big data, artificial intelligence (AI), and machine learning, facilitating investment opportunities. However, it is considered to have been introduced and evolved as the Bitcoin protocol in 2009. Digital assets date with the coining of ‘bit gold.’ This was formulated as a decentralised digital currency. The aim behind introducing such a currency was to have a payment mechanism outside the vigilance of the central authority, with a recurring high value. This laid the foundation of blockchain technology, inaugurating the market in digital assets. The utilisation of distributed ledger technology attracted a lot of financial players towards it. Creating digital assets and their required funding subsists on ongoing market changes and investor demands. Computer codes form the skeletal framework of digital assets and depend on agreement-based computer algorithms for effectuating transactions. These digital assets include an almost exhaustive range of virtual assets. Shifting the discussion to the "good faith acquisition" regime, when the transferee of property acquires a good title irrespective of the defect in the title of the transferor, it is known as "good faith acquisition" or "negotiability," an exception to the general rule: nemo dat quod non habet ("no one gives what they don't have"). In this scenario, good faith acquisition (negotiability) is quite relevant as digital assets transfer functions based on quite complex structures, which can be deceiving in disputes arising from insolvency, etc. This has been brought to light with the example of a digital asset held in custody by a third-party intermediary. Here, the legal status of the asset is uncertain, which creates the problem of determining who has the right to claim ownership over it. This is where the new legal framework for digital assets is needed to clear the air and award digital tokens the status of ‘property’ regarding these issues. The proposed legal framework can take due recourse to legal concepts, such as the law of bailment. This paper seeks to analyse and establish the legitimate status of virtual digital assets globally, slowly transitioning to a virtually driven e-community.

Fully higher-order coupling of finite element and level set methods for two-phase flow with a new explicit projection method

This work presents the development of a higher-order finite element method for modeling unsteady, incompressible and inviscid two-phase flows using the level set method. A new explicit projection method is introduced to solve the incompressible Euler equation, where the pressure and the velocity fields are solved separately. To implement high order temporal schemes, the Hodge decomposition in this projection method is not applied to the velocity but to its time derivative. A discontinuous Galerkin discretization is used to solve both the level set and the nonlinear advection terms present in the Euler equations, while a Galerkin discretization is used to solve the pressure Poisson equation. The present method is a fully high order numerical approach, both in space and temporal dimensions. Numerical results provided by several 2D benchmark test cases in naval hydrodynamics (i.e hydrostatic equilibrium, sloshing and dam break) validate the computational code, illustrating the power of the proposed method. Compared to the CFD codes based on low order VOF/finite-volume methods, the present approach demonstrates greater accuracy on coarse meshes, leading to reduced CPU time.

Force convective heat transfer simulation in a rectangular channel with multiple square obstacles via finite element method

This study investigated fluid flow and forced convective heat transfer in rectangular microchannels with square barriers, as illustrated in Fig. 1 . In the first situation, three obstacles were positioned along the microchannel’s top wall. In the second scenario, obstacles were positioned along the microchannel’s bottom wall. In the final example, three square obstacles are placed symmetrically on either side of the microchannel wall. With the help of the Finite Element Method (FEM), we investigate the physicochemical behavior of the microchannel. The development of computer code within COMSOL multiphysics made it possible to simulate heat transport and fluid flow. The results include the implications of the rarefaction effect on fluid flow and heat transmission and decisions regarding the location of barriers and the shape of obstacles in squares. In addition, with the lowest value in skin friction and a lower Nusselt number, the third example, which has barriers on both sides, provides a valuable method for reducing the fluid temperature at the exit of the microchannel. This is because it has barriers on both sides. In the section under “Results and Discussion,” we provide an in-depth analysis of the numerical data derived from the microchannel. • Objective: This study aims to numerically investigate forced convective heat transfer in a rectangular channel featuring multiple square obstacles. The simulation employs the FEM to model fluid flow and temperature distribution. • Geometry and complexity: The channel geometry incorporates square obstacles, introducing geometric complexity. The FEM is chosen for its capability to handle intricate geometries accurately.

Translating nanoEHS data using EPA NaKnowBase and the resource description framework.

The U.S. Federal Government has supported the generation of extensive amounts of nanomaterials and related nano Environmental Health and Safety (nanoEHS) data, there is a need to make these data available to stakeholders. With recent efforts, a need for improved interoperability, translation, and sustainability of Federal nanoEHS data in the United States has been realized. The NaKnowBase (NKB) is a relational database containing experimental results generated by the EPA Office of Research and Development (ORD) regarding the actions of engineered nanomaterials on environmental and biological systems. Through the interaction of the National Nanotechnology Initiative's Nanotechnology Environmental Health Implications (NEHI) Working Group, and the Database and Informatics Interest Group (DIIG), a U.S. Federal nanoEHS Consortium has been formed. The primary goal of this consortium is to establish a "common language" for nanoEHS data that aligns with FAIR data standards. A second goal is to overcome nomenclature issues inherent to nanomaterials data, ultimately allowing data sharing and interoperability across the diverse U.S. Federal nanoEHS data compendium, but also in keeping a level of consistency that will allow interoperability with U.S. and European partners. The most recent version of the EPA NaKnowBase (NKB) has been implemented for semantic integration. Computational code has been developed to use each NKB record as input, modify and filter table data, and subsequently output each modified record to a Research Description Framework (RDF). To improve the accuracy and efficiency of this process the EPA has created the OntoSearcher tool. This tool partially automates the ontology mapping process, thereby reducing onerous manual curation. Here we describe the efforts of the US EPA in promoting FAIR data standards for Federal nanoEHS data through semantic integration, as well as in the development of NAMs (computational tools) to facilitate these improvements for nanoEHS data at the Federal partner level.

Taylor Polynomials in a High Arithmetic Precision as Universal Approximators

Function approximation is a fundamental process in a variety of problems in computational mechanics, structural engineering, as well as other domains that require the precise approximation of a phenomenon with an analytic function. This work demonstrates a unified approach to these techniques, utilizing partial sums of the Taylor series in a high arithmetic precision. In particular, the proposed approach is capable of interpolation, extrapolation, numerical differentiation, numerical integration, solution of ordinary and partial differential equations, and system identification. The method employs Taylor polynomials and hundreds of digits in the computations to obtain precise results. Interestingly, some well-known problems are found to arise in the calculation accuracy and not methodological inefficiencies, as would be expected. In particular, the approximation errors are precisely predictable, the Runge phenomenon is eliminated, and the extrapolation extent may a priory be anticipated. The attained polynomials offer a precise representation of the unknown system as well as its radius of convergence, which provides a rigorous estimation of the prediction ability. The approximation errors are comprehensively analyzed for a variety of calculation digits and test problems and can be reproduced by the provided computer code.

An analytical study on the effect of different photovoltaic technologies on enviro-economic parameter and energy metrics of active solar desalting unit

The enviro-economic and energy metrics analyses of the solar system by incorporating different photovoltaic technologies are required to fulfill the sustainable development goal of the United Nations. However, the effect of different photovoltaic technologies on the solar distillation system has not been informed by any of the researchers to date. This research gap has been explored in this study. The present manuscript is an endeavour to explore the best photovoltaic technology aimed at a single-slope solar desalting unit comprised of N identical photovoltaic thermal flat-plate-collectors. Data regarding all four weather conditions for a year for New Delhi's climate have been procured from the Indian Meteorological Department situated in the western part of India. Fundamental equations along with input data have been provided to a computational code developed in MATLAB for the estimation of various performance parameters. Concludingly, the energy payback time is minimum (1.64 years) for the case of copper indium gallium selenide photovoltaic technology, however, maximum values of life cycle conversion efficiency and net CO2 mitigation are respectively 0.171 and 199.10 tons of CO2 for the case of crystalline silicon photovoltaic technology. The carbon credit is also found to be a maximum (15269.25 US$) for crystalline silicon photovoltaic technology.

Towards reusable building blocks for agent-based modelling and theory development

Despite the increasing use of standards for documenting and testing agent-based models (ABMs) and sharing of open access code, most ABMs are still developed from scratch. This is not only inefficient, but also leads to ad hoc and often inconsistent implementations of the same theories in computational code and delays progress in the exploration of the functioning of complex social-ecological systems (SES). We argue that reusable building blocks (RBBs) known from professional software development can mitigate these issues. An RBB is a submodel that represents a particular mechanism or process that is relevant across many ABMs in an application domain, such as plant competition in vegetation models, or reinforcement learning in a behavioural model. RBBs need to be distinguished from modules, which represent entire subsystems and include more than one mechanism and process. While linking modules faces the same challenges as integrating different models in general, RBBs are “atomic” enough to be more easily re-used in different contexts. We describe and provide examples from different domains for how and why building blocks are used in software development, and the benefits of doing so for the ABM community and to individual modellers. We propose a template to guide the development and publication of RBBs and provide example RBBs that use this template. Most importantly, we propose and initiate a strategy for community-based development, sharing and use of RBBs. Individual modellers can have a much greater impact in their field with an RBB than with a single paper, while the community will benefit from increased coherence, facilitating the development of theory for both the behaviour of agents and the systems they form. We invite peers to upload and share their RBBs via our website - preferably referenced by a DOI (digital object identifier obtained e.g. via Zenodo). After a critical mass of candidate RBBs has accumulated, feedback and discussion can take place and both the template and the scope of the envisioned platform can be improved.

Assessing Learning of Computer Programing Skills in the Age of Generative Artificial Intelligence.

Generative artificial intelligence (AI) tools such as ChatGPT, Bard, and Claude have recently become a concern in the delivery of engineering education. For courses focused on computer coding, such tools are capable for creating working computer code across a range of computer languages and computing platforms. In a course for mechanical engineers focused on C++ coding for the Arduino microcontroller and coding engineering problems in Matlab, a new approach to assessment of programing homework assignments was developed. This assessment moved the focus of assigned points from the correctness of the code to the effort and understanding of the code demonstrated by the student during in-person grading. Students who participated fully in in-person grading did significantly better on a midterm exam. Relative to a previous semester, where grading was focused on correct code, students had a slightly higher average midterm exam score. This approach appears to be effective in supporting computational learning in the face of evolving tools that could be used to circumvent learning. Future work should examine how to also encourage responsible use of generative AI in computational learning.

Simple Method for Predicting the Heats of Formation of Energetic Metal–Organic Frameworks

The heats of formation (HOF) of energetic materials is crucial in determining their performance. A novel approach has been introduced to accurately predict the condensed phase HOF of energetic metal-organic frameworks (EMOFs). The experimental condensed phase HOF data of 63 EMOFs were utilized to develop and evaluate the model. This model relies on particular chemical bonds, elemental composition, and some of the specific structural parameters that can be obtained from the chemical structures of EMOFs. This model can be easily applied to EMOFs with intricate compositions and structures, without the need for complicated computer codes. Statistical parameters along with the internal and external validation approaches were employed to assess the accuracy, precision, goodness-of-fit, and goodness-of-prediction of the model. Values of the coefficients of determination in training set, as well as internal and external validation, i.e., R2, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{Q}}_{{{\ ext{LOO}}}}^{2}$$\\end{document}, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{Q}}_{{5{\ ext{-fold}}}}^{2}$$\\end{document}, and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R_{{{\ ext{External}}}}^{2}$$\\end{document} are 0.96, 0.93, 0.91, and 0.94, respectively.

Failure Pressure Evaluation of Corroded Pipeline Using Semi-Empirical and Finite Element Analysis

Abstract Computational simulation using the finite element method (FEM) has proven to be one of the most efficient methods for the correct evaluation of the structural integrity of pipelines with corrosion-induced defects. In this paper, the results of the failure pressure of corroded pipelines obtained with a computer tool called PIPEFLAW based on nonlinear finite element analyses are presented and compared with the failure pressures of corroded pipes obtained with twelve (12) advanced semi-empirical models from the literature. A computational code was created in the Python language to automatically obtain the failure pressure of randomly generated cases of corroded pipes, which allows the analysis of multiple piping cases with isolated external corrosion defects in an idealized configuration. For this purpose, the LHS (Latin Hypercube Sampling) approach was adopted for the design of experiments to obtain a better distribution of cases in the sample space. Comparisons between the results using the FEM and semi-empirical models show, as expected, that the semi-empirical solutions are more conservative in most cases analyzed in this work. A statistical model is proposed that adheres to the relative error distribution for some of the models used. Moreover, the influence of some parameters (pipeline thickness, defect depth, defect length, defect width, yield stress and ultimate stress) on the failure pressure was evaluated. It was found that in most of the studied models the error increases as the defect depth increases.

Ensemble Learning for Robust Malware Detection in the Windows 7 Environment

In today's digital era, the exponential growth in the dissemination of malicious software presents a formidable and pervasive threat to individuals and organizations alike. Malware, comprising of computer code or scripts, exhibits a propensity to override computer systems and engage in unauthorized activities, such as the illicit extraction of valuable information. The magnitude of such control is particularly perilous in the contemporary landscape. The rapid and constant evolution of malware compounds this risk, necessitating the development and implementation of sophisticated countermeasures to combat this pressing issue. In response to this challenge, this study has turned to the utilization of ensemble learning techniques as a means of effectively and accurately identifying and detecting malware. Specifically, the investigation focuses on the classification of applications on the MS Windows 7 and 8 operating systems as either malicious or benign, by leveraging static and dynamic features extracted from these applications. The foundational models employed in this study consist of recurrent neural networks (RNNs), trained on the dynamic features of malware, and convolutional neural networks (CNNs), and trained on the static features of malware. This comprehensive approach ensures that no malware goes undetected. Multiple techniques for constructing an ensemble model, such as Boosting, Stacking, and Bagging, are thoroughly examined and analyzed. Ultimately, the Bagging technique is deemed the most suitable and is consequently implemented in this study.