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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.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

CDK4/6 inhibition enhances SHP2 inhibitor efficacy and is dependent upon RB function in malignant peripheral nerve sheath tumors

Malignant peripheral nerve sheath tumors (MPNSTs) are highly aggressive soft tissue sarcomas with limited treatment options, and new effective therapeutic strategies are desperately needed. We observe antiproliferative potency of genetic depletion of PTPN11 or pharmacological inhibition using the SHP2 inhibitor (SHP2i) TNO155. Our studies into the signaling response to SHP2i reveal that resistance to TNO155 is partially mediated by reduced RB function, and we therefore test the addition of a CDK4/6 inhibitor (CDK4/6i) to enhance RB activity and improve TNO155 efficacy. In combination, TNO155 attenuates the adaptive response to CDK4/6i, potentiates its antiproliferative effects, and converges on enhancement of RB activity, with greater suppression of cell cycle and inhibitor-of-apoptosis proteins, leading to deeper and more durable antitumor activity in in vitro and in vivo patient-derived models of MPNST, relative to either single agent. Overall, our study provides timely evidence to support the clinical advancement of this combination strategy in patients with MPNST and other tumors driven by loss of NF1.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

GAAF: Searching Activation Functions for Binary Neural Networks Through Genetic Algorithm

Binary neural networks (BNNs) show promising utilization in cost and power-restricted domains such as edge devices and mobile systems. This is due to its significantly less computation and storage demand, but at the cost of degraded performance. To close the accuracy gap, in this paper we propose to add a complementary activation function (AF) ahead of the sign based binarization, and rely on the genetic algorithm (GA) to automatically search for the ideal AFs. These AFs can help extract extra information from the input data in the forward pass, while allowing improved gradient approximation in the backward pass. Fifteen novel AFs are identified through our GA-based search, while most of them show improved performance (up to 2.54% on ImageNet) when testing on different datasets and network models. Interestingly, periodic functions are identified as a key component for most of the discovered AFs, which rarely exist in human designed AFs. Our method offers a novel approach for designing general and application-specific BNN architecture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Analysis of Salmonella enterica Isolated from a Mixed-Use Watershed in Georgia, USA: Antimicrobial Resistance, Serotype Diversity, and Genetic Relatedness to Human Isolates

As the cases of Salmonella enterica infections associated with contaminated water are increasing, this study was conducted to address the role of surface water as a reservoir of S. enterica serotypes. We sampled rivers and streams (n = 688) over a 3-year period (2015 to 2017) in a mixed-use watershed in Georgia, USA, and 70.2% of the total stream samples tested positive for Salmonella. A total of 1,190 isolates were recovered and characterized by serotyping, antimicrobial susceptibility testing, and pulsed-field gel electrophoresis (PFGE). A wide range of serotypes was identified, including those commonly associated with humans and animals, with S. enterica serotype Muenchen being predominant (22.7%) and each serotype exhibiting a high degree of strain diversity by PFGE. About half (46.1%) of the isolates had PFGE patterns indistinguishable from those of human clinical isolates in the CDC PulseNet database. A total of 52 isolates (4.4%) were resistant to antimicrobials, out of which 43 isolates were multidrug resistant (MDR; resistance to two or more classes of antimicrobials). These 52 resistant Salmonella isolates were screened for the presence of antimicrobial resistance genes, plasmid replicons, and class 1 integrons, out of which four representative MDR isolates were selected for whole-genome sequencing analysis. The results showed that 28 MDR isolates resistant to 10 antimicrobials had blacmy-2 on an A/C plasmid. Persistent contamination of surface water with a high diversity of Salmonella strains, some of which are drug resistant and genetically indistinguishable from human isolates, supports a role of environmental surface water as a reservoir for and transmission route of this pathogen.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A matheuristic for design and dispatch of a utility-connected distributed energy system

Modeling distributed power generation systems often requires complicated mathematical expressions that present challenges for commercial optimization solvers. Here, this paper presents a matheuristic to solve a mixed-integer optimization model that informs decisions regarding the design and dispatch of a utility-connected microgrid. We deploy a genetic algorithm to search the system design space and a linear program to solve the economic dispatch problem. The model is a component of a web tool that requires solutions within a few minutes. Our method yields objective function values within 5% of an exogenously produced optimal in fewer than 30 seconds for 90% of our test cases compared to only 10% of our test cases by a traditional optimization solver in the same amount of time.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Improving CRISPR/Cas9-mediated genome editing efficiency in Yarrowia lipolytica using direct tRNA-sgRNA fusions

We report Yarrowia lipolytica is an important oleaginous yeast currently used in the production of specialty chemicals and has a great potential for further applications in lipid biotechnology. Harnessing the full potential of Y. lipolytica is, however, limited by its inherent recalcitrance to genetic manipulation. In contrast to Saccharomyces cerevisiae, Y. lipolytica is poor in homology-mediated DNA repair and thus in homologous recombination, which limits site-specific gene editing in this yeast. Recently developed CRISPR/Cas9-based methods using tRNA-sgRNA fusions succeeded in editing some genomic loci in Y. lipolytica. Nonetheless, the majority of other tested loci either failed editing or editing was achieved but at very low efficiency using these methods. Using tools of secondary RNA structure prediction, we were able to improve the design of the tRNA-sgRNA fusions used for the expression of single guide RNA (sgRNA) in such methods. This resulted in high efficiency CRISPR/cas9 gene editing at chromosomal loci that failed gene editing or were edited at very low efficiencies with previous methods. In addition, we characterized the gene editing performance of our newly designed tRNA-sgRNA fusions for both chromosomal gene integration and deletion. As such, this study presents an efficient CRISPR/Cas9-mediated gene-editing tool for efficient genetic engineering of Yarrowia lipolytica.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

The engineered single guide RNA structure as a biomarker for gene-editing reagent exposure

Abstract CRISPR arrays and CRISPR-associated (Cas) proteins comprise a prevalent adaptive immune system in bacteria and archaea. These systems defend against exogenous parasitic mobile genetic elements. The adaption of single effector CRISPR-Cas systems has massively facilitated gene-editing due to the reprogrammable guide RNA. The guide RNA affords little priming space for conventional PCR-based nucleic acid tests without foreknowledge of the spacer sequence. Further impeding detection of gene-editor exposure, these systems are derived from human microflora and pathogens ( Staphylococcus pyogenes , Streptococcus aureus , etc.) that contaminate human patient samples. The single guide RNA—formed from the CRISPR RNA (crRNA) and transactivating RNA (tracrRNA)—harbors a variable tetraloop sequence between the two RNA segments, complicating PCR assays. Identical single effector Cas proteins are used for gene-editing and naturally by bacteria. Antibodies raised against these Cas proteins are unable to distinguish CRISPR-Cas gene-editors from bacterial contaminant. To overcome the high potential for false positives, we have developed a DNA displacement assay to specifically detect gene-editors. We leveraged the single guide RNA structure as an engineered moiety for gene-editor exposure that does not cross-react with bacterial CRISPRs. Our assay has been validated for five common CRISPR systems and functions in complex sample matrices.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Expanding the synthetic biology toolbox of Cupriavidus necator for establishing fatty acid production

Abstract The Gram-negative betaproteobacterium Cupriavidus necator is a chemolithotroph that can convert carbon dioxide into biomass. Cupriavidus necator has been engineered to produce a variety of high-value chemicals in the past. However, there is still a lack of a well-characterized toolbox for gene expression and genome engineering. Development and optimization of biosynthetic pathways in metabolically engineered microorganisms necessitates control of gene expression via functional genetic elements such as promoters, ribosome binding sites (RBSs), and codon optimization. In this work, a set of inducible and constitutive promoters were validated and characterized in C. necator, and a library of RBSs was designed and tested to show a 50-fold range of expression for green fluorescent protein (gfp). The effect of codon optimization on gene expression in C. necator was studied by expressing gfp and mCherry genes with varied codon-adaptation indices and was validated by expressing codon-optimized variants of a C12-specific fatty acid thioesterase to produce dodecanoic acid. We discuss further hurdles that will need to be overcome for C. necator to be widely used for biosynthetic processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

COVID-19 Biomarkers and Advanced Sensing Technologies for Point-of-Care (POC) Diagnosis

COVID-19, also known as SARS-CoV-2 is a novel, respiratory virus currently plaguing humanity. Genetically, at its core, it is a single-strand positive-sense RNA virus. It is a beta-type Coronavirus and is distinct in its structure and binding mechanism compared to other types of coronaviruses. Testing for the virus remains a challenge due to the small market available for at-home detection. Currently, there are three main types of tests for biomarker detection: viral, antigen and antibody. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) remains the gold standard for viral testing. However, the lack of quantitative detection and turnaround time for results are drawbacks. This manuscript focuses on recent advances in COVID-19 detection that have lower limits of detection and faster response times than RT-PCR testing. The advancements in sensing platforms have amplified the detection levels and provided real-time results for SARS-CoV-2 spike protein detection with limits as low as 1 fg/mL in the Graphene Field Effect Transistor (FET) sensor. Additionally, using multiple biomarkers, detection levels can achieve a specificity and sensitivity level comparable to that of PCR testing. Proper biomarker selection coupled with nano sensing detection platforms are key in the widespread use of Point of Care (POC) diagnosis in COVID-19 detection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Topology optimization of an airfoil fin microchannel heat exchanger using artificial intelligence

High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, is analyzed and optimized using a design of experiments with artificial intelligence techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin printed circuit heat exchangers (PCHEs) has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure is developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. Computational fluid dynamics is used to analyze a simplified two-channel configuration where five design parameters are varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods (a 5D polynomial and a regression neural network) are compared for generating surrogate models and the resulting response surface approximation is input to a genetic algorithm that is used to identify a set of optimal parameters. The optimal geometries are found across six channel Reynolds numbers ranging from 1000 to 5000, since inlet conditions affect flow through the heat exchanger. Additionally, a set of optimal designs that maximizes heat transfer and minimizes pressure drop is identified, and a thermal stress analysis is performed on the optimal design. Correlations for the Nusselt number and Darcy friction factor are developed that can be useful for thermal hydraulic analyses using system codes. Thermal stresses are analyzed and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.

42 ENGINEERING↗
DOE OSTI2023

Engineered Escherichia coli platforms for tyrosine-derivative production from phenylalanine using phenylalanine hydroxylase and tetrahydrobiopterin-regeneration system

Aromatic compounds derived from tyrosine are important and diverse chemicals that have industrial and commercial applications. Although these aromatic compounds can be obtained by extraction from natural producers, their growth is slow, and their content is low. To overcome these problems, many of them have been chemically synthesized from petroleum-based feedstocks. However, because of the environmental burden and depleting availability of feedstock, microbial cell factories are attracting much attention as sustainable and environmentally friendly processes. To facilitate development of microbial cell factories for producing tyrosine derivatives, we developed simple and convenient tyrosine-producing Escherichia coli platforms with a bacterial phenylalanine hydroxylase, which converted phenylalanine to tyrosine with tetrahydromonapterin as a cofactor, using a synthetic biology approach. By introducing a tetrahydrobiopterin-regeneration system, the tyrosine titer of the plasmid-based engineered strain was 4.63 g/L in a medium supplemented with 5.00 g/L phenylalanine with a test tube. The strains were successfully used to produce industrially attractive compounds, such as tyrosol with a yield of 1.58 g/L by installing a tyrosol-producing module consisting of genes encoding tyrosine decarboxylase and tyramine oxidase on a plasmid. Gene integration into E. coli chromosomes has an advantage over the use of plasmids because it increases genetic stability without antibiotic feeding to the culture media and enables more flexible pathway engineering by accepting more plasmids with artificial pathway genes. Therefore, we constructed a plasmid-free tyrosine-producing platform by integrating five modules, comprising genes encoding the phenylalanine hydroxylase and tetrahydrobiopterin-regeneration system, into the chromosome. The platform strain could produce 1.04 g/L of 3,4-dihydroxyphenylalanine, a drug medicine, by installing a gene encoding tyrosine hydroxylase and the tetrahydrobiopterin-regeneration system on a plasmid. Moreover, by installing the tyrosol-producing module, tyrosol was produced with a yield of 1.28 g/L. We developed novel E. coli platforms for producing tyrosine from phenylalanine at multi-gram-per-liter levels in test-tube cultivation. The platforms allowed development and evaluation of microbial cell factories installing various designed tyrosine-derivative biosynthetic pathways at multi-grams-per-liter levels in test tubes.

09 BIOMASS FUELS↗
DOE OSTI2023

Genomic characterization of rare earth binding by Shewanella oneidensis

Abstract Rare earth elements (REE) are essential ingredients of sustainable energy technologies, but separation of individual REE is one of the hardest problems in chemistry today. Biosorption, where molecules adsorb to the surface of biological materials, offers a sustainable alternative to environmentally harmful solvent extractions currently used for separation of rare earth elements (REE). The REE-biosorption capability of some microorganisms allows for REE separations that, under specialized conditions, are already competitive with solvent extractions, suggesting that genetic engineering could allow it to leapfrog existing technologies. To identify targets for genomic improvement we screened 3,373 mutants from the whole genome knockout collection of the known REE-biosorbing microorganism Shewanella oneidensis MR-1. We found 130 genes that increased biosorption of the middle REE europium, and 112 that reduced it. We verified biosorption changes from the screen for a mixed solution of three REE (La, Eu, Yb) using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) in solution conditions with a range of ionic strengths and REE concentrations. We identified 18 gene ontologies and 13 gene operons that make up key systems that affect biosorption. We found, among other things, that disruptions of a key regulatory component of the arc system ( hptA ), which regulates cellular response to anoxic environments and polysaccharide biosynthesis related genes ( wbpQ , wbnJ , SO_3183 ) consistently increase biosorption across all our solution conditions. Our largest total biosorption change comes from our SO_4685 , a capsular polysaccharide (CPS) synthesis gene, disruption of which results in an up to 79% increase in biosorption; and nusA, a transcriptional termination/anti-termination protein, disruption of which results in an up to 35% decrease in biosorption. Knockouts of glnA , pyrD , and SO_3183 produce small but significant increases (≈ 1%) in relative biosorption affinity for ytterbium over lanthanum in multiple solution conditions tested, while many other genes we explored have more complex binding affinity changes. Modeling suggests that while these changes to lanthanide biosorption selectivity are small, they could already reduce the length of repeated enrichment process by up to 27%. This broad exploratory study begins to elucidate how genetics affect REE-biosorption by S. oneidensis , suggests new areas of investigation for better mechanistic understanding of the membrane chemistry involved in REE binding, and offer potential targets for improving biosorption and separation of REE by genetic engineering.

Medin, Sean↗
DOE OSTI2022

Quantum model learning agent: characterisation of quantum systems through machine learning

Accurate models of real quantum systems are important for investigating their behaviour, yet are difficult to distil empirically. Here, we report an algorithm—the quantum model learning agent (QMLA)—to reverse engineer Hamiltonian descriptions of a target system. We test the performance of QMLA on a number of simulated experiments, demonstrating several mechanisms for the design of candidate Hamiltonian models and simultaneously entertaining numerous hypotheses about the nature of the physical interactions governing the system under study. QMLA is shown to identify the true model in the majority of instances, when provided with limited a priori information, and control of the experimental setup. Our protocol can explore Ising, Heisenberg and Hubbard families of models in parallel, reliably identifying the family which best describes the system dynamics. We demonstrate QMLA operating on large model spaces by incorporating a genetic algorithm to formulate new hypothetical models. The selection of models whose features propagate to the next generation is based upon an objective function inspired by the Elo rating scheme, typically used to rate competitors in games such as chess and football. In all instances, our protocol finds models that exhibit F 1 score ≥ 0.88 when compared with the true model, and it precisely identifies the true model in 72% of cases, whilst exploring a space of over 250 000 potential models. By testing which interactions actually occur in the target system, QMLA is a viable tool for both the exploration of fundamental physics and the characterisation and calibration of quantum devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Development of modular expression across phylogenetically distinct diazotrophs

Diazotrophic bacteria can reduce atmospheric nitrogen into ammonia enabling bioavailability of the essential element. Many diazotrophs closely associate with plant roots increasing nitrogen availability, acting as plant growth promoters. These associations have the potential to reduce the need for costly synthetic fertilizers if they could be engineered for agricultural applications. However, despite the importance of diazotrophic bacteria, genetic tools are poorly developed in a limited number of species, in turn narrowing the crops and root microbiomes that can be targeted. Here, we report optimized protocols and plasmids to manipulate phylogenetically diverse diazotrophs with the goal of enabling synthetic biology and genetic engineering. Three broad-host-range plasmids can be used across multiple diazotrophs, with the identification of one specific plasmid (containing origin of replication RK2 and a kanamycin resistance marker) showing the highest degree of compatibility across bacteria tested. We then demonstrated modular expression by testing seven promoters and eleven ribosomal binding sites using proxy fluorescent proteins. Finally, we tested four small molecule inducible systems to report expression in three diazotrophs and demonstrated genome editing in Klebsiella michiganensis M5al.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Estimation of Arrivals on Green at Signalized Intersections Using Stop-Bar Video Detection

Across the world, traffic congestion is increasing with alarming rapidity. Traffic signal control effectiveness, in coordinated networks, is often investigated in relation to the type of vehicle arrivals at the signalized intersections. Recently, several transportation agencies have switched from traditional loop detectors to video detection. When video cameras are accompanied by computer vision, one can extract more information about traffic “dynamics” than by using traditional inductive loop detectors. Collecting arrival times of multiple vehicles after the first arrival at the stop-bar detector might be challenging when using inductive loop detectors (since after the first arrival, detector status is always occupied). However, emerging video detection systems allow tracking of each vehicle’s entrance time in the detection zone, departure time from the detection zone, and the type of vehicle. This information can be used to estimate vehicular arrival and departure times, which then can be fed into machine learning algorithms to estimate arrivals on green (AOG). However, such research ideas have not been documented so far. Thus, this paper presents an estimation model for AOG, which was developed using multigene genetic programming. A robust experimental dataset was collected from a highly calibrated and validated microsimulation model of an 11-intersection corridor in Chattanooga, TN. The results of the model’s performance analysis showed the high accuracy of the training-, testing-, and validation datasets. The practical benefit of this model is that it can be applied to estimate arrival types at intersections where only stop-bar video detection exists.

Engineering↗
DOE OSTI2022

Optimal Power Dispatch of DGs in Radial and Mesh AC Grids: A Hybrid Solution Methodology between the Salps Swarm Algorithm and Successive Approximation Power Flow Method

In this paper, we address the problem of the optimal power dispatch of Distributed Generators (DGs) in Alternating Current (AC) networks, better known as the Optimal Power Flow (OPF) problem. We used, as the objective function, the minimization of power losses (P loss ) associated with energy transport, which are subject to the set of constraints that compose AC networks in an environment of distributed generation. To validate the effectiveness of the proposed methodology in solving the OPF problem in any network topology, we employed one 10-node mesh test system and three radial text systems: 10, 33, and 69 nodes. In each test system, DGs were allowed to inject 20%, 40%, and 60% of the power supplied by the slack generator in the base case. To solve the OPF problem, we used a master–slave methodology that integrates the optimization method Salps Swarm Algorithm (SSA) and the load flow technique based on the Successive Approximation (SA) method. Moreover, for comparison purposes, we employed some of the algorithms reported in the specialized literature to solve the OPF problem (the continuous genetic algorithm, the particle swarm optimization algorithm, the black hole algorithm, the antlion optimization algorithm, and the Multi-Verse Optimizer algorithm), which were selected because of their excellent results in solving such problems. The results obtained by the proposed solution methodology demonstrate its superiority and convergence capacity in terms of minimization of P loss in both radial and mesh systems. It provided the best reduction in minimum P loss in short processing times and showed excellent repeatability in each test system and scenario under analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Next-Generation Sequencing Data from a CUT&RUN Study of R. toruloides IFO0880 Cse4 and Orc1 Binding Sites

Rhodotorula toruloides has been increasingly explored as a host for bioproduction of lipids, fatty acid derivatives and terpenoids. Various genetic tools have been developed, but neither a centromere nor an autonomously replicating sequence (ARS), both necessary elements for stable episomal plasmid maintenance, has yet been reported. In this study, cleavage under targets and release using nuclease (CUT&RUN), a method used for genome-wide mapping of DNA–protein interactions, was used to identify R. toruloides IFO0880 genomic regions associated with the centromeric histone H3 protein Cse4, a marker of centromeric DNA. Fifteen putative centromeres ranging from 8 to 19 kb in length were identified and analyzed, and four were tested for, but did not show, ARS activity. These centromeric sequences contained below average GC content, corresponded to transcriptional cold spots, were primarily nonrepetitive and shared some vestigial transposon-related sequences but otherwise did not show significant sequence conservation. Future efforts to identify an ARS in this yeast can utilize these centromeric DNA sequences to improve the stability of episomal plasmids derived from putative ARS elements.

Genome Engineering↗
DOE OSTI2021

Large-scale survey of prion protein genetic variability in scrapie disease-free goats from the United States

Scrapie is a slowly progressive neurodegenerative disease of small ruminants caused by an accumulation of an abnormal isoform of prion protein in the central nervous system. Polymorphisms of the prion protein gene (PRNP) strongly modulate scrapie resistance and incubation period in goats. The aim of this study was to identify PRNP genetic variability in goats across the United States. Blood from a total of 6,029 apparent scrapie disease-free goats from 654 operations and 19 breeds were analyzed. Sequencing of PRNP revealed 26 genotypes with different rates based on eight codons. The GG127, RR154, and QQ222 genotypes were predominant and showed a remarkably high rate across all goats. The QK222 and NS146 genotypes, known to be protective against scrapie, were found in 0.6% [with 95% CI = (0.3, 1.2)] and 22.0% [95% CI = (19.1, 25.2)] of goats, respectively. The QK222 genotype was found in 23.1% of Oberhasli goats tested, with 95%CI = (3.9, 68.7)] and 22.0% of Toggenburg goats tested with 95%CI = (9.7, 42.5)], while NS146 was found in 65.5% of Savannah goats tested, with 95%CI = (30.8, 89.9), 36.7% of Boer goats tested, with 95%CI = (33.1, 40.4), 36.3% of Nubian goats tested, with 95%CI = (27.0, 46.7)], and 35.6% of LaMancha goats tested, with 95%CI = (22.8, 50.8%). The MM142 and IM142 genotypes were found more frequently in goats on dairy operations, while the HR143, NS146, and ND146 genotypes were found more frequently in goats on meat operations. Goats in the east region had a higher percentage of goats with RH154, RQ211, and QK222 genotypes than goats in the west region. The results of this study showed high genetic variability of PRNP among the U.S. goat population, with differences by location and breed, and may serve as a rationale for development of goat breeding programs at the national level to mitigate the risk of scrapie.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Constitutive model development of aluminum alloy 1100 for elevated temperature forming process

Commercially pure aluminum alloy, AA1100, presents good electrical and thermal conductivity, high formability, and low cost. Those favorable characteristics have the potential to enable bipolar plates with improved economics and enhanced performance compared to current stainless steel bipolar plates for proton exchange membrane fuel cells. An accurate constitutive model is essential to develop and optimize processing parameters and effectively control the forming process. Here, the objective of this work is to develop a constitutive model of AA1100 that is able to simulate stress-strain relation, formed geometry, and predict the onset of fracture strain to avoid forming failure. Initially, a set of tensile tests at temperature between 300 and 500°C and strain rate between 0.005 and 1.0/s were conducted to examine the deformation behavior. Then, a set of damage-based unified visco-plastic constitutive equations is proposed and calibrated based on the results of stress-strain data. A genetic algorithm optimization method is applied to search for best fitting material constants in constitutive equations. The proposed model shows good predictability of both the stress-strain relation and fracture strain at low strain rate and high temperature conditions. The accuracy of proposed model is also evaluated statistically. A comparison of the proposed model with three popular models (Arrhenius-type mode, Johnson-Cook model and Zerilli-Armstrong model) was made. The proposed model shows the best experimental agreement with correlation coefficient of 0.96 in contrast to 0.25, 0.38 and 0.75 for the popular models, respectively. The proposed model can help to optimize the elevated temperature forming process and guide die design to enable optimal geometric features in the formed components.

08 HYDROGEN↗