Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “genetics”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 595 records · Page 33

Trends in lignin modification: a comprehensive analysis of the effects of genetic manipulations/mutations on lignification and vascular integrity

A comprehensive assessment of lignin configuration in transgenic and mutant plants is long overdue. This review thus undertook the systematic analysis of trends manifested through genetic and mutational manipulations of the various steps associated with monolignol biosynthesis; this included consideration of the downstream effects on organized lignin assembly in the various cell types, on vascular function/integrity, and on plant growth and development. As previously noted for dirigent protein (homologs), distinct and sophisticated monolignol forming metabolic networks were operative in various cell types, tissues and organs, and form the cell-specific guaiacyl (G) and guaiacyl-syringyl (G-S) enriched lignin biopolymers, respectively. Regardless of cell type undergoing lignification, carbon allocation to the different monolignol pools is apparently determined by a combination of phenylalanine availability and cinnamate-4-hydroxylase/"p-coumarate-3-hydroxylase" (C4H/C3H) activities, as revealed by transcriptional and metabolic profiling. Downregulation of either phenylalanine ammonia lyase or cinnamate-4-hydroxylase thus predictably results in reduced lignin levels and impaired vascular integrity, as well as affecting related (phenylpropanoid-dependent) metabolism. Depletion of C3H activity also results in reduced lignin deposition, albeit with the latter being derived only from hydroxyphenyl (H) units, due to both the guaiacyl (G) and syringyl (S) pathways being blocked. Apparently the cells affected are unable to compensate for reduced G/S levels by increasing the amounts of H-components. The downstream metabolic networks for G-lignin enriched formation in both angiosperms and gymnosperms utilize specific cinnamoyl CoA O-methyltransferase (CCOMT), 4-coumarate:CoA ligase (4CL), cinnamoyl CoA reductase (CCR) and cinnamyl alcohol dehydrogenase (CAD) isoforms: however, these steps neither affect carbon allocation nor H/G designations, this being determined by C4H/C3H activities. Such enzymes thus fulfill subsidiary processing roles, with all (except CCOMT) apparently being bifunctional for both H and G substrates. Their severe downregulation does, however, predictably result in impaired monolignol biosynthesis, reduced lignin deposition/vascular integrity, (upstream) metabolite build-up and/or shunt pathway metabolism. There was no evidence for an alternative acid/ester O-methyltransferase (AEOMT) being involved in lignin biosynthesis.The G/S lignin pathway networks are operative in specific cell types in angiosperms and employ two additional biosynthetic steps to afford the corresponding S components, i.e. through introduction of an hydroxyl group at C-5 and its subsequent O-methylation. [These enzymes were originally classified as ferulate-5-hydroxylase (F5H) and caffeate O-methyltransferase (COMT), respectively.] As before, neither step has apparently any role in carbon allocation to the pathway; hence their individual downregulation/manipulation, respectively, gives either a G enriched lignin or formation of the well-known S-deficient bm3 "lignin" mutant, with cell walls of impaired vascular integrity. In the latter case, COMT downregulation/mutation apparently results in utilization of the isoelectronic 5-hydroxyconiferyl alcohol species albeit in an unsuccessful attempt to form G-S lignin proper. However, there is apparently no effect on overall G content, thereby indicating that deposition of both G and S moieties in the G/S lignin forming cells are kept spatially, and presumably temporally, fully separate. Downregulation/mutation of further downstream steps in the G/S network [i.e. utilizing 4CL, CCR and CAD isoforms] gives predictable effects in terms of their subsidiary processing roles: while severe downregulation of 4CL gave phenotypes with impaired vascular integrity due to reduced monolignol supply, there was no evidence in support of increased growth and/or enhanced cellulose biosynthesis. CCR and CAD downregulation/mutations also established that a depletion in monolignol supply reduced both lignin contents supply reduced both lignin contents and vascular integrity, with a concomitant shift towards (upstream) metabolite build-up and/or shunting.The extraordinary claims of involvement of surrogate monomers (2-methoxybenzaldehyde, feruloyl tyramine, vanillic acid, etc.) in lignification were fully disproven and put to rest, with the investigators themselves having largely retracted former claims. Furthermore analysis of the well-known bm1 mutation, a presumed CAD disrupted system, apparently revealed that both G and S lignin components were reduced. This seems to imply that there is no monolignol specific dehydrogenase, such as the recently described sinapyl alcohol dehydrogenase (SAD) for sinapyl alcohol formation. Nevertheless, different CAD isoforms of differing homology seem to be operative in different lignifying cell types, thereby giving the G-enriched and G/S-enriched lignin biopolymers, respectively. For the G-lignin forming network, however, the CAD isoform is apparently catalytically less efficient with all three monolignols than that additionally associated with the corresponding G/S lignin forming network(s), which can more efficiently use all three monolignols. However, since CAD does not determine either H, G, or S designation, it again serves in a subsidiary role-albeit using different isoforms for different cell wall developmental and cell wall type responses.The results from this analysis contrasts further with speculations of some early investigators, who had viewed lignin assembly as resulting from non-specific oxidative coupling of monolignols and subsequent random polymerization. At that time, though, the study of the complex biological (biochemical) process of lignin assembly had begun without any of the (bio)chemical tools to either address or answer the questions posed as to how its formation might actually occur. Today, by contrast, there is growing recognition of both sophisticated and differential control of monolignol biosynthetic networks in different cell types, which serve to underscore the fact that complexity of assembly need not be confused any further with random formation. Moreover, this analysis revealed another factor which continues to cloud interpretations of lignin downregulation/mutational analyses, namely the serious technical problems associated with all aspects of lignin characterization, whether for lignin quantification, isolation of lignin-enriched preparations and/or in determining monomeric compositions. For example, in the latter analyses, some 50-90% of the lignin components still cannot be detected using current methodologies, e.g. by thioacidolysis cleavage and nitrobenzene oxidative cleavage. This deficiency in lignin characterization thus represents one of the major hurdles remaining in delineating how lignin assembly (in distinct cell types) and their configuration actually occurs.

Review, Academic↗

Genetic ablation of root cap cells in Arabidopsis

The root cap is increasingly appreciated as a complex and dynamic plant organ. Root caps sense and transmit environmental signals, synthesize and secrete small molecules and macromolecules, and in some species shed metabolically active cells. However, it is not known whether root caps are essential for normal shoot and root development. We report the identification of a root cap-specific promoter and describe its use to genetically ablate root caps by directing root cap-specific expression of a diphtheria toxin A-chain gene. Transgenic toxin-expressing plants are viable and have normal aerial parts but agravitropic roots, implying loss of root cap function. Several cell layers are missing from the transgenic root caps, and the remaining cells are abnormal. Although the radial organization of the roots is normal in toxin-expressing plants, the root tips have fewer cytoplasmically dense cells than do wild-type root tips, suggesting that root meristematic activity is lower in transgenic than in wild-type plants. The roots of transgenic plants have more lateral roots and these are, in turn, more highly branched than those of wild-type plants. Thus, root cap ablation alters root architecture both by inhibiting root meristematic activity and by stimulating lateral root initiation. These observations imply that the root caps contain essential components of the signaling system that determines root architecture.

Non-NASA Center↗

Genetically Controlled Iron Oxide Biomineralization in Encapsulin Nanocompartments for Magnetic Manipulation of a Mammalian Cell Line

Magnetic nanoparticles have proven invaluable for biomechanical investigations due to their ability to exert localized forces. However, cellular delivery of exogenous magnetic agents often results in endosomal entrapment, thereby limiting their utility for manipulating subcellular structures. This study characterizes and exploits fully genetically controlled biomineralization of iron-oxide cores inside encapsulin nanocompartments to enable magnetic-activated cell sorting (MACS) and magnetic cell manipulation. The fraction of MACS-retained cells showed substantial overexpression of encapsulins and exhibited both para- and ferrimagnetic responses with magnetic moments of 10 -15 A m 2 per cell, comparable to standard exogenous labels for MACS. Electron microscopy revealed that MACS-retained cells contained densely packed agglomerates of ≈30 nm iron oxide cores consisting of ultrafine quasicrystalline ordered nuclei within an amorphous matrix of iron, oxygen, and phosphorus. Scanning transmission X-ray microscopy, X-ray absorption spectroscopy, and Raman microspectroscopy confirmed that the iron-oxide species are consistent with ferric oxide (Fe 2 O 3 ). In addition, the encapsulin-overexpressing MACS-retained cells can be manipulated by a magnetic needle and regrown in patterns determined by magnetic gradients. This study demonstrates that the formation of quasicrystalline iron oxide with mixed para/ferrimagnetic behavior in the cytosol of mammalian cells enables magnetic manipulation without the delivery of exogenous agents.

60 APPLIED LIFE SCIENCES↗

Rapid and Efficient Genetic Transformation of Sorghum via Agrobacterium -Mediated Method

Genetic transformation via Agrobacterium-mediated methodology has been used in many sorghum studies. However, the transformation efficiency still varies significantly due to high dependence on sorghum genotypes and technical expertise. In this article, we describe a sorghum transformation procedure in sufficient detail using a public genotype, P898012. This system utilizes a standard binary transgenic vector carrying the bar gene as a selectable marker and immature embryos as starting explants. Glufosinate is employed as the selective agent during callus and shoot induction. Further, this procedure is relatively rapid, efficient, highly reproducible, and should be applicable for many other sorghum genotypes

Agrobacterium-mediated↗

Genetic encoding of 3–nitro–tyrosine reveals the impacts of 14–3–3 nitration on client binding and dephosphorylation

14–3–3 proteins are central hub regulators of hundreds of phosphorylated “client” proteins. They are subject to over 60 post–translational modifications (PTMs), yet little is known how these PTMs alter 14–3–3 function and its ability to regulate downstream signaling pathways. An often neglected, but well–documented 14–3–3 PTM found under physiological and immune–stimulatory conditions is the conversion of tyrosine to 3–nitro–tyrosine at several Tyr sites, two of which are located at sites considered important for 14–3–3 function: Y130 (β–isoform numbering) is located in the primary phospho–client peptide–binding groove, while Y213 is found on a secondary binding site that engages with clients for full 14–3–3/client complex formation and client regulation. By genetically encoding 3–nitro–tyrosine, we sought to understand if nitration at Y130 and Y213 effectively modulated 14–3–3 structure, function, and client complexation. The 1.5 Å resolution crystal structure of 14–3–3 nitrated at Y130 showed the nitro group altered the conformation of key residues in the primary binding site, while functional studies confirmed client proteins failed to bind this variant of 14–3–3. But, in contrast to other client–binding deficient variants, it did not localize to the nucleus. Further, the 1.9 Å resolution structure of 14–3–3 nitrated at Y213 revealed unusual flexibility of its C–terminal α–helix resulting in domain swapping, suggesting additional structural plasticity though its relevance is not clear as this nitrated form retained its ability to bind clients. Collectively, our data suggest that nitration of 14–3–3 will alter downstream signaling systems, and if uncontrolled could result in global dysregulation of the 14–3–3 interactome.

14-3-3↗

Optimization of groundwater artificial recharge systems using a genetic algorithm: a case study in Beijing, China

An optimization approach is used for the operation of groundwater artificial recharge systems in an alluvial fan in Beijing, China. The optimization model incorporates a transient groundwater flow model, which allows for simulation of the groundwater response to artificial recharge. The facilities’ operation with regard to recharge rates is formulated as a nonlinear programming problem to maximize the volume of surface water recharged into the aquifers under specific constraints. This optimization problem is solved by the parallel genetic algorithm (PGA) based on OpenMP, which could substantially reduce the computation time. To solve the PGA with constraints, the multiplicative penalty method is applied. In addition, the facilities’ locations are implicitly determined on the basis of the results of the recharge-rate optimizations. Two scenarios are optimized and the optimal results indicate that the amount of water recharged into the aquifers will increase without exceeding the upper limits of the groundwater levels. Optimal operation of this artificial recharge system can also contribute to the more effective recovery of the groundwater storage capacity.

58 GEOSCIENCES↗

Computer-Aided Diagnosis of Lung Nodules in Computed Tomography by Using Phylogenetic Diversity, Genetic Algorithm, and SVM

Lung cancer is pointed as the major cause of death among patients with cancer throughout the world. This work is intended to develop a methodology for diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. In order to differentiate between the patterns of malignant and benign nodules, we used phylogenetic diversity by means of particular indexes, that are: intensive quadratic entropy, extensive quadratic entropy, average taxonomic distinctness, total taxonomic distinctness, and pure diversity indexes. After that, we applied the genetic algorithm for selection of the best model. In the tests’ stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules. The proposed work presents promising results at the classification into malignant and benign, achieving accuracy of 92.52%, sensitivity of 93.1% and specificity of 92.26%. The results demonstrated a good rate of correct detections using texture features. Since a precocious detection allows a faster therapeutic intervention, thus a more favorable prognostic to the patient, we propose herein a methodology that contributes to the area in this aspect.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm

The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended on their experience only, so the computer-aided technology is very important to aid with the diagnosis accuracy. In this study, to improve the performance of tumor detection, we investigated comparative approach of different segmentation techniques and selected the best one by comparing their segmentation score. Further, to improve the classification accuracy, the genetic algorithm is employed for the automatic classification of tumor stage. The decision of classification stage is supported by extracting relevant features and area calculation. The experimental results of proposed technique are evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on segmentation score, accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 92.03% accuracy, 91.42% specificity, 92.36% sensitivity, and an average segmentation score between 0.82 and 0.93 demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 93.79% dice similarity index coefficient, which indicates better overlap between the automated extracted tumor regions with manually extracted tumor region by radiologists.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Application of Genetic Algorithm (GA) Assisted Partial Least Square (PLS) Analysis on Trilinear and Non-trilinear Fluorescence Data Sets to Quantify the Fluorophores in Multifluorophoric Mixtures: Improving Quantification Accuracy of Fluorimetric Estimations of Dilute Aqueous Mixtures

Excitation-emission matrix fluorescence (EEMF) and total synchronous fluorescence spectroscopy (TSFS) are the 2 fluorescence techniques that are commonly used for the analysis of multifluorophoric mixtures. These 2 fluorescence techniques are conceptually different and provide certain advantages over each other. The manual analysis of such highly correlated large volume of EEMF and TSFS towards developing a calibration model is difficult. Partial least square (PLS) analysis can analyze the large volume of EEMF and TSFS data sets by finding important factors that maximize the correlation between the spectral and concentration information for each fluorophore. However, often the application of PLS analysis on entire data sets does not provide a robust calibration model and requires application of suitable pre-processing step. The present work evaluates the application of genetic algorithm (GA) analysis prior to PLS analysis on EEMF and TSFS data sets towards improving the precision and accuracy of the calibration model. The GA algorithm essentially combines the advantages provided by stochastic methods with those provided by deterministic approaches and can find the set of EEMF and TSFS variables that perfectly correlate well with the concentration of each of the fluorophores present in the multifluorophoric mixtures. The utility of the GA assisted PLS analysis is successfully validated using (i) EEMF data sets acquired for dilute aqueous mixture of four biomolecules and (ii) TSFS data sets acquired for dilute aqueous mixtures of four carcinogenic polycyclic aromatic hydrocarbons (PAHs) mixtures. In the present work, it is shown that by using the GA it is possible to significantly improve the accuracy and precision of the PLS calibration model developed for both EEMF and TSFS data set. Hence, GA must be considered as a useful pre-processing technique while developing an EEMF and TSFS calibration model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Removal of Congo Red from Aqueous Solution by Hydroxyapatite Nanoparticles Loaded on Zein as an Efficient and Green Adsorbent: Response Surface Methodology and Artificial Neural Network-Genetic Algorithm

This study is based on the application of hydroxyapatite nanoparticles loaded on Zein (Zein/nHAp) as an efficient adsorbent for the removal of Congo red from aqueous solutions. The properties of the adsorbent were characterized using various techniques including FT-IR, XRD, FE-SEM, and BET. The influence of five parameters such as pH, temperature, contact time, initial dye concentration, and adsorbent dosage on the removal percentage of the dye was investigated. The optimum conditions of 5.83, 34.32 °C, 5.20 min, 392.10 ppm, and 0.007 g were achieved for pH, temperature, contact time, initial dye concentration, and adsorbent dosage, respectively. The maximum removal percentage of 99.48% was obtained under the optimum condition that exhibited high adsorption potential of the used adsorbent. Central composite design (CCD) under response surface methodology and artificial neural network-genetic algorithm (ANN-GA) were utilized for optimization of parameters. Comparison of the results of the two models in terms of coefficient of determination (R{sup 2}) and mean absolute percentage error confirmed the prediction potential of CCD and ANN-GA. Higher ability and accuracy of ANN-GA in prediction was found based on given results. The experimental equilibrium data were studied by Langmuir, Freundlich, Temkin and Dubinin-Radushkevic isotherm models and explored that the data well presented by Langmuir model with maximum adsorption capacity of 416.66 mg/g. The adsorption kinetic was well-fitted by the pseudo-second-order model. The thermodynamics of the adsorption displayed spontaneous and endothermic nature of the process. Regeneration investigation showed that Zein/nHAp can impressively be reused, indicating that the adsorbent was a promising one for the removal of Congo red from aqueous solution. Graphical Abstract: .

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Hybrid Inverse Parameter Identification of Fully Coupled Ductile Damage Model for Steel Sheet DP600 with Two Different Algorithms: Trust Region and Genetic Algorithms

Ductile damage model has been widely regarded as a valuable method to predict the failure of sheet metal. Based on the thermodynamic theory and continuum damage mechanics, the fully coupled ductile damage model can be developed, which also can better predict the initiation and growth of the fracture. But the identifications of model parameters with theoretical methodology are difficult due to the complex coupling relationships existing among all state variables. The inverse methodology is regarded as a good method to resolve the problem. In this paper, the recently proposed fully coupled ductile damage model is chosen to investigate the deformation behavior of DP600 steel, in which the mixed saturation isotropic and kinematic hardenings are taken into account and fully coupled with the ductile damage. During the identification process, the least square formula of the error between experimental and numerical results is selected as the target function. The trust region algorithm and genetic algorithm are used with the help of MATLAB for the identification of three damage parameters. Finally, by comparing the experimental and numerical results, the validations of two algorithms are proved. The efficiency of the optimization process with trust region algorithm is higher, but with lower accuracy. Meanwhile, the optimization process is greatly affected by the chosen initial values of the ductile damage parameters.

36 MATERIALS SCIENCE↗

Functional coding and non-coding variants in human BRCA1 gene and their use in genetic screening

BRCA1 is involved in double-strand DNA damage repair pathways, and mutations in the gene are associated with hereditary breast and ovarian cancers. With great help of the development of high-throughput DNA sequencing techniques numerous single-nucleotide polymorphisms (SNPs) and insertion deletion (Indel) mutations are detected on both coding and non-coding/regulatory regions of the BRCA1. Mutations may cause pathogenic or benign changes on the protein function or affect its expression. In the last decade, use of genetic screening tests to detect mutations on such genes has become greatly popular. However, it is very important to know the effect of the detected mutations, which is mostly possible by the use of predictive softwares, and also the related family history to be able to correctly analyse the screening results and to inform the patient. Therefore, use of in silico and in vitro techniques to score the pathogenicity of detected variants on genes like BRCA1 is now of great importance. Otherwise, results obtained from screening tests and family history cannot be analysed precisely.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

New approach based on group technology for the consolidation problem in cloud computing-mathematical model and genetic algorithm

Resource management is a hotspot issue in distributed systems like cloud computing (CC). It means how to prepare the computational resources, i.e., servers and virtual machines (VMS), to execute the tasks. This paper offers a new approach based on Group Technology (GT)—known as a powerful philosophy for the resource management in cellular manufacturing systems—to deal with the resource management problem in CC. We develop a mathematical model to optimally consolidate the VMs, servers and tasks simultaneously to control several important factors such as task migrations and server load variation, as well as the number of VMs. To test the validity of our proposed model, several small problems are generated randomly and solved by LINGO 9 software. Furthermore, to cope with larger problems, which cannot be solved optimally, a genetic algorithm is proposed. We, finally, compare our methods with the most well-known algorithms in this context, round robin (RR) and first-come, first-served (FCFS) algorithms.

97 MATHEMATICS AND COMPUTING↗

Optimization of the FRIB beam dump: a hybrid genetic algorithm and reinforcement learning approach

The operational envelope of high-power-density systems, such as particle accelerators and advanced nuclear energy systems, is critically constrained by the need to manage extreme thermal loads. To address this, we present a novel hybrid optimization framework combining a genetic algorithm (GA) with a soft actor-critic (SAC) deep reinforcement learning agent. This framework was applied to a practical high-heat-flux problem: redesigning the beam dump at the Facility for Rare Isotope Beams (FRIB) for a power upgrade from 20 kW to 50 kW. The resulting design, validated by three-dimensional conjugate heat transfer simulations, suppresses hazardous hot spots and yields a markedly more uniform temperature distribution. This provides a robust operating margin, increasing the average power-handling capability by 72% relative to the current design, demonstrating the framework’s potential to solve complex thermal management challenges in both accelerator technology and advanced nuclear systems.

Accelerator↗

Process design and economics for production of advanced biofuels from genetically modified lipid-producing sorghum

This study evaluates the potential for making advanced biofuels from genetically modified (GM) lipid–producing sorghum. A biodiesel coproduction process is developed to extract, purify, and upgrade lipids to diesel fuel while carbohydrates are utilized for making ethanol through acid thermal pretreatment, enzymatic hydrolysis, and fermentation. To assess the advantages of coproducing biodiesel from GM–sorghum, process economics are compared to a cellulosic ethanol biorefinery receiving non-GM sorghum. Minimum ethanol selling prices (MESP) that reach a breakeven point after 30 years of service life are calculated as an economic index to compare the two processes. Results indicate that biodiesel coproduction improves the economics by lowering the MESP from 3.08/gal for the ethanol-only process to 2.46/gal. Sensitivity analyses reveal that increasing sorghum’s lipid content, increasing the lipid extraction efficiency, and reducing the solvent-to-solids ratio in lipid extraction columns are the most important process parameters to further enhance technoeconomics. Analyses indicate that a lipid content above 13 wt% (dry basis) or a biomass price less than 65/Mg (dry basis) will result in a 2014 ethanol wholesale price of 2.25/gal for the coproduction process.

09 BIOMASS FUELS↗

Artificial intelligence based analysis of nanoindentation load–displacement data using a genetic algorithm

In this work, we developed an automated tool, Nanoindentation Neo package for the analysis of nanoindentation load–displacement curves using a Genetic Algorithm (GA) applied to the Oliver-Pharr method (Oliver et al.,1992). For some materials, such as polycrystalline isotropic graphites, Least Squares Fitting (LSF) of the unload curve can produce unrealistic fit parameters. These graphites exhibit sharply peaked unloading curves not easily fit using the LSF, which tends to overestimate the indenter tip geometry parameter. To tackle this problem, we extended our general materials characterization tool Neo for EXAFS analysis (Terry et al., 2021) to fit nanoindentation data. Nanoindentation Neo automatically processes and analyzes nanoindentation data with minimal user input while producing meaningful fit parameters. GA, a robust metaheuristic method, begins with a population of temporary solutions using model parameters called chromosomes; from these we evaluate a fitness value for each solution, and select the best solutions to mix with random solutions producing the next generation. A mutation operator then modifies existing solutions by random perturbations, and the optimal solution is selected. We tested the GA method using Silica and Al reference standards. We fit samples of graphite and a high entropy alloy (HEA) consisting of BCC and FCC phases.

42 ENGINEERING↗

Developing a robust strength model using physically-informed genetic programming

The strength of materials is influenced by a range of external conditions, such as temperature and deformation rate. Consequently, materials that demonstrate substantial variations in their mechanical behavior due to fluctuations in temperature and strain rate require complex strength models to accurately predict material performance in real-world applications. To predict such complex behavior, a robust and flexible strength model is necessary. In this work, we utilize genetic programming-based symbolic regression (GPSR) to develop data-driven strength models that accurately represent the measured stress–strain responses of tin across a wide range of strain, strain rate and temperature regimes. The GPSR models are constrained by physically-informed conditions, which leads to significant improvement in extrapolation. The best model is integrated into a multi-physics code to perform Taylor impact simulations, validating the model’s accuracy and robustness. In conclusion, the model predictions showed excellent agreement with experimental results, particularly when compared to predictions using traditional strength models.

Genetic programming↗

Optimization of Type 4 composite pressure vessels using genetic algorithms and simulated annealing

We propose and demonstrate two methodologies for weight minimization of Type 4 (plastic lined, fiber wrapped) compressed hydrogen pressure vessels: genetic algorithms and simulated annealing. We consider 70 MPa vessels with a safety factor of 2.25, and analyze the vessels with classical laminate theory. We propose an objective function based on Tsai-Wu criterion, composite thickness, a safety factor, and a penalization factor. Here, the optimum results are analyzed and compared by a high resolution finite element model. Computer simulations show that the proposed methodology produces more efficient designs by reducing the weight by up to 9.8% and 11.2% when compared to previously published vessel optimization research.

08 HYDROGEN↗