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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Efficacy and safety of 3-week response-guided triple direct-acting antiviral therapy for chronic hepatitis C infection: a phase 2, open-label, proof-of-concept study

In order to shorten the course of direct-acting antiviral agents for chronic hepatitis C virus (HCV) infection, we examined the antiviral efficacy and safety of 3 weeks of response-guided therapy with an NS3 protease inhibitor and dual NS5A inhibitor–NS5B nucleotide analogue. In this open-label, phase 2a, single centre study, Chinese patients with chronic HCV genotype 1b infection without cirrhosis were randomly allocated by a computer program to one of three treatment groups (sofosbuvir, ledipasvir, and asunaprevir; sofosbuvir, daclatasvir, and simeprevir; or sofosbuvir, daclatasvir, and asunaprevir) until six patients in each group (1:1:1) achieved an ultrarapid virological response (plasma HCV RNA <500 IU/mL by day 2, measured by COBAS TaqMan HCV test, version 2.0). Patients with an ultrarapid virological response received 3 weeks of therapy. Patients who did not achieve an ultrarapid response were switched to sofosbuvir and ledipasvir for either 8 weeks or 12 weeks. Furthermore, the primary endpoint was the proportion of patients with a sustained virological response at 12 weeks (SVR12) after treatment completion, analysed in the intention-to-treat population. All patients who achieved an ultrarapid virological response were included in the safety analysis. This trial is registered with ClinicalTrials.gov, number NCT02470858. Between April 5, 2015, and April 15, 2015, 26 eligible patients were recruited. 12 patients were assigned to sofosbuvir, ledipasvir, and asunaprevir; six to sofosbuvir, daclatasvir, and simeprevir; and eight to sofosbuvir, daclatasvir, and asunaprevir. Six patients in each group achieved an ultrarapid virological response (18 [69%]). All patients with an ultrarapid virological response who were given 3 weeks of triple therapy achieved SVR12. The most common adverse events were fatigue (one [17%] of six patients receiving sofosbuvir, ledipasvir, and asunaprevir; one [17%] of six patients receiving sofosbuvir, daclatasvir, and simeprevir; and two [33%] of six patients receiving sofosbuvir, daclatasvir, and asunaprevir) and headache (one [17%] patient in each group). There were no patients who experienced any serious adverse events. In this proof-of-concept study, all patients with chronic HCV without cirrhosis who achieved an ultrarapid virological response on triple direct-acting antiviral regimens by day 2 and received 3 weeks of treatment were cured, with excellent tolerability. By shortening the duration of therapy from the currently recommended 12 weeks to 3 weeks, we could drastically reduce the cost of therapy and the rate of adverse events. Further large-scale studies should be done to confirm our findings.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

CFM-ID 4.0: More Accurate ESI MS/MS Spectral Prediction and Compound Identification

In the field of metabolomics, mass spectrometry (MS) is the method most commonly used for identifying and annotating metabolites. As this typically involves matching a given MS spectrum against an experimentally acquired reference spectral library, this approach is limited by the coverage and size of such libraries (which typically number in the thousands). These experimental libraries can be greatly extended by predicting the MS spectra of known chemical structures (which number in the millions) to create computational reference spectral libraries. To facilitate the generation of predicted spectral reference libraries we developed CFM-ID, a computer program that can accurately predict ESI-MS/MS spectrum for a given compound structure. CFM-ID is one of the best-performing methods for compound-to-mass-spectrum prediction, and also one of the top tools for in silico mass-spectrum-to-compound identification. This work improves CFM-ID’s ability to predict ESI-MS/MS spectra from compounds by: (1) learning parameters from features based on the molecular topology, (2) adding a new approach to ring cleavage that models such cleavage as a sequence of simple chemical bond dissociations and (3) expanding its hand-written rule-based predictor to cover more chemical classes, including acylcarnitines, acylcholines, flavonols, flavones, flavanones, and flavonoid glycosides. We demonstrate that this new version of CFM-ID (version 4.0) is significantly more accurate than previous CFM-ID versions, in terms of both EI-MS/MS spectral prediction and compound identification. CFM-ID 4.0 is available at http://cfmid4.wishartlab.com/ as a webservice and docker images can be downloaded at https://hub.docker.com/r/wishartlab/cfmid

Wang, Fei↗
DOE OSTI2019

Molecular Simulation of the Catalytic Regeneration of n BuLi through a Hydrometalation Route

Efficient regeneration of organolithium compounds is a challenging aspect in the process of novel organometathetical catalytic cycles. A new method for Mg production from seawater has been proposed that capitalizes on the rich chemistry of Grignard reagents. Based on a 3-step catalytic cycle with Cp2MClL catalyst (M=Ti, Zr and L select organic ligands), it requires the regeneration of nBuLi. The potential of the proposed approach is evaluated with density functional theory based molecular simulations. The results reveal that the high affinity of Li towards Cl and N results in the formation of alkanes, and the strong coupling of catalyst and BuLi leads to catalyst deactivation. To improve its catalytic performance, we proposed the use of a diamine co-catalyst and a modified catalyst with a ligand that does not contain N, which would help release BuLi from the vicinity of the catalyst. Ab inito molecular dynamics simulations at 298 K in explicit solvent (THF) were used to estimate the Gibb’s free energetics and equilibrium constants obtained from the vibrational density of states. The results show a marked improvement in the free-energetics with lower barriers towards the completion of the catalytic cycle and suppression of de-activation channels. M.-S. L. and B. P. M. were supported by the US Department of Energy (DOE), Advanced Research Projects Agency for Energy performed at the Pacific Northwest National Laboratory (PNNL). V.-A. G. and R. R. were supported by DOE, Office of Science, Office of Basic Energy Sciences-CGBS Catalysis Program. Computational resources were provided by PNNL Research Computing Cluster and the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231.

Lee, Mal-Soon (ORCID:000000016851177X)↗
DOE OSTI2016

Dependence of the rate of LiF ion pairing on the description of molecular interaction

We present an analysis of the dynamics of ion-pairing of Lithium Fluoride (LiF) in aqueous solvent using both detailed molecular simulation as well as reduced models within a Gener- alized Langevin Equation (GLE) framework. We explored the sensitivity of the ion-pairing phenomena to the details of descriptions of molecular interaction, comparing two empirical potentials to explicit quantum based density functional theory. We find quantitative differences in the potentials of mean force for ion-pairing as well as time dependent frictions that lead to variations in the rate constant and reactive flux correlation functions. These details reflect differences in solvent response to ion-pairing between different representations of molecular interaction and influence anharmonicity of the dynamic response. We find that the short time anharmonic response is recovered with a GLE parameterization. Recovery of the details of long time response may require extensions to the reduced model. We show that the utility of using a reduced model leads to a straight forward application of variational transition state the- ory concepts to the condensed phase system. The significance of this is reflected in the analysis of committor distributions and the variation of planar hypersurfaces, leading to an improved understanding of factors that determine the rate of LiF ion-pairing. CJM and GKS are supported by the U.S. Department of Energy‘s (DOE) Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences. Pacific Northwest Na- tional Laboratory (PNNL) is operated for the Department of Energy by Battelle. MDB is grateful for the support of Laboratory Directed Research and Development funding under the auspices of PNNL’s Laboratory Initiative Materials Synthesis and Simulation across Scales (MS3). Additional computing resources were generously allocated by PNNL’s Institutional Computing program. EP acknowledges support from PNNL’s Alternate Sponsored Fellowship program and IMPRS Dres- den.Support to P.J. from the Czech Science Foundation (grant P208/12/G016) and the Academy of Sciences (Praemium Academie award) is gratefully acknowledged.

Pluharova, Eva↗
DOE OSTI2020

Nanometer-Scale Correlations in Aqueous Salt Solutions

The intermediate-range (1–4 nm) correlation of cations, an- ions, and water in aqueous alkaline earth salt solutions is measured using synchrotron X-ray diffraction. We differentiate from the entire solution structure factor, S(Q), a separate region at low Q (<1 °A-1) containing local diffraction maxima (pre-peaks) that indicate nanometer-scale oscillatory behavior. These features are quantitatively reproduced with classical molecular dynamics simulations. At high concentrations, the pre-peaks emerge from correlations arising from existence of small quasi close-packed lattice- like structures comprised of cation hydration spheres. We also analyze the concentration dependence of the pre-peak and discuss the overall results in light of the rich literature dealing with intermediate-range correlations underlying universal phenomena in concentrated electrolytes. The authors were supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences. Pacific Northwest National Laboratory (PNNL) is operated by Battelle for the U.S. Department of Energy under Contract No. DE-AC05-76RL01830. Computing resources were generously allocated by PNNL's Institutional Computing program. DOE Contract No. DEAC02- 06CH11357 supports operation of the Advanced Photon Source at Argonne National Laboratory.

Chemical physics, nucleation, Madelung potential↗
DOE OSTI2013

DFT+U Study on the Localized Electronic States and Their Potential Role During H2O Dissociation and CO Oxidation Processes on CeO2(111) Surface

We present the results of an extensive density functional theory based electronic structure study of the role of 4f-state localized electron states in the surface chemistry of a partially reduced CeO2(111) surface. These electrons exist as polaronic states, residing of Ce3+ sites, which can be created by either the formation of oxygen vacancies, OV, or other surface defects. Via ab initio molecular dynamics these localized electrons are found to be able to move freely within the upper surface layer but penetration into the bulk is prohibited as a result of the higher elastic strain induced by creating a subsurface Ce3+ defect. We found that water molecules can be easily dissociated into two surface bound hydroxyls at the Ce4+ site associated with OV sites and the dissociation process does not significantly affect the electronic structure of the reduced surface. In the presence of water, a proton-mediated Mars-van Krevelen mechanism for CO oxidation via the formation of bicarbonate species is identified. Overall we find that surface hydroxyls formed via water dissociation at CeO2 surface lead to inhabitation of the CO oxidation reaction. This is consistent with the experimental observation of requisite elevated temperatures, on the order of 600K, for this reaction to occur. Part of this work was supported by the US Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences & Biosciences and performed at the Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated for DOE by Battelle. J. Li and Y.-G. Wang were also financially supported by NKBRSF (2011CB932400) and NSFC (91026003, 21101098) of China. Y.-G. Wang acknowledges the fellowship from China Scholarship Council and the PNNL-ASF fellowship program. Computational resources were provided at W. R. Wiley Environmental Molecular Science Laboratory (EMSL), a national scientific user facility sponsored by the Department of Energy’s Office of Biological and Environmental Research located at PNNL and the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2025

DeFault: DEep‐Learning‐Based FAULT Delineation Using the IBDP Passive Seismic Data at the Decatur CO2 Storage Site

Abstract The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection offer vital insights into subsurface structures and the ability to monitor fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high‐quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, , specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain‐adaptation, allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using , the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of on a field case study involving injection related microseismic data from Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and could aid in potential damage induced by seismicity. Our results highlight the potential of as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS, illustrating machine learning’s capacity to refine these methods. Ultimately, our work has significant implications for CCUS technology deployment, an essential strategy in combating climate change. Plain Language Summary In our quest to tackle climate change, we use a strategy known as carbon capture, utilization, and storage (CCUS) to keep greenhouse gases out of the atmosphere. This strategy relies heavily on our ability to understand what's happening deep under the earth's surface. To make sure we store super critical safely, we need to accurately map out the geological structure, especially faults, but this is tough without high‐quality data and complex computer programs. We've developed a new tool called “DeFault,” which uses advanced machine learning to improve how we find and map these underground features. “DeFault” is smart enough to learn from numerically simulated data and then apply what it’s learned to real‐world situations. It groups together seismic activity—tiny tremors and shifts in the earth—based on when and where they happen, which helps us spot where there might be cracks or faults. We tested “DeFault” in Illinois, where CO 2 is injected underground, and it successfully pinpointed where these tremors occurred and mapped out the faults, helping to prevent accidents accurately in the future. Our study shows that “DeFault” will be a powerful ally in making CCUS safer and more effective, especially for the Illinois Basin Decatur Project. Key Points Faults and fractures introduced by carbon storage can be monitored by passive seismicity DeFault algorithm enables an automatic process for accurate and efficient passive seismic event locating and clustering

58 GEOSCIENCES↗
DOE OSTI2021

Crystallography companion agent for high-throughput materials discovery

The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone and impossible to scale. With the advent of autonomous robotic scientists or self-driving laboratories, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which outputs probabilistic classifications—rather than absolutes—to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering substantial time savings. It is demonstrated on a diverse set of organic and inorganic materials characterization challenges. This method is directly applicable to inverse design approaches and robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Theoretical rotation-vibration spectrum of thioformaldehyde

We present a variational calculation of the first comprehensive T = 300 K rovibrational line list for thioformaldehyde, H{sub 2}CS. It covers 41 809 rovibrational levels for states up to J{sub max} = 30 with vibrational band origins up to 5000 cm{sup −1} and provides the energies and line intensities for 547 926 transitions from the ground vibrational state to these levels. It is based on our previously reported accurate ab initio potential energy surface and a newly calculated ab initio dipole moment surface. Minor empirical adjustments are made to the ab initio equilibrium geometry to reduce systematic errors in the predicted intra-band rotational energy levels. The rovibrational energy levels and transition intensities are computed variationally by using the methods implemented in the computer program TROVE. Transition wavelengths and intensities are found to be in excellent agreement with the available experimental data. The present calculations correctly reproduce the observed resonance effects, such as intensity borrowing, thus reflecting the high accuracy of the underlying ab initio surfaces. We report a detailed analysis of several vibrational bands, especially those complicated by strong Coriolis coupling, to facilitate future laboratory assignments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

New approach for calibration the efficiency of HpGe detectors

This work evaluates the efficiency calibrating of HpGe detector coupled with Canberra GC3018 with Genie 2000 software and Ortec GEM25-76-XLB-C with Gamma Vision software; available at Neutron activation analysis laboratory in Malaysian Nuclear Agency (NM). The efficiency calibration curve was constructed from measurement of an IAEA, standard gamma–point sources set composed by {sup 214}Am, {sup 57}Co, {sup 133}Ba, {sup 152}Eu, {sup 137}Cs and {sup 60}Co. The efficiency calibrations were performed for three different geometries: 5, 10 and 15 cm distances from the end cap detector. The polynomial parameters functions were simulated through a computer program, MATLAB in order to find an accurate fit to the experimental data points. The efficiency equation was established from the known fitted parameters which allow for the efficiency evaluation at particular energy of interest. The study shows that significant deviations in the efficiency, depending on the source-detector distance and photon energy.

42 ENGINEERING↗
DOE OSTI2015

Quantum dynamics study on the CHIPR potential energy surface for the hydroperoxyl radical: The reactions O + OH⇋O{sub 2} + H

Quantum scattering calculations of the O({sup 3}P)+OH({sup 2}Π)⇌O{sub 2}({sup 3}Σ{sub g}{sup −})+H({sup 2}S) reactions are presented using the combined-hyperbolic-inverse-power-representation potential energy surface [A. J. C. Varandas, J. Chem. Phys. 138, 134117 (2013)], which employs a realistic, ab initio-based, description of both the valence and long-range interactions. The calculations have been performed with the ABC time-independent quantum reactive scattering computer program based on hyperspherical coordinates. The reactivity of both arrangements has been investigated, with particular attention paid to the effects of vibrational excitation. By using the J-shifting approximation, rate constants are also reported for both the title reactions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Adapting algebraic diagrammatic construction schemes for the polarization propagator to problems with multi-reference electronic ground states exploiting the spin-flip ansatz

For the investigation of molecular systems with electronic ground states exhibiting multi-reference character, a spin-flip (SF) version of the algebraic diagrammatic construction (ADC) scheme for the polarization propagator up to third order perturbation theory (SF-ADC(3)) is derived via the intermediate state representation and implemented into our existing ADC computer program adcman. The accuracy of these new SF-ADC(n) approaches is tested on typical situations, in which the ground state acquires multi-reference character, like bond breaking of H{sub 2} and HF, the torsional motion of ethylene, and the excited states of rectangular and square-planar cyclobutadiene. Overall, the results of SF-ADC(n) reveal an accurate description of these systems in comparison with standard multi-reference methods. Thus, the spin-flip versions of ADC are easy-to-use methods for the calculation of “few-reference” systems, which possess a stable single-reference triplet ground state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Ro-vibrational averaging of the isotropic hyperfine coupling constant for the methyl radical

We present the first variational calculation of the isotropic hyperfine coupling constant of the carbon-13 atom in the CH{sub 3} radical for temperatures T = 0, 96, and 300 K. It is based on a newly calculated high level ab initio potential energy surface and hyperfine coupling constant surface of CH{sub 3} in the ground electronic state. The ro-vibrational energy levels, expectation values for the coupling constant, and its temperature dependence were calculated variationally by using the methods implemented in the computer program TROVE. Vibrational energies and vibrational and temperature effects for coupling constant are found to be in very good agreement with the available experimental data. We found, in agreement with previous studies, that the vibrational effects constitute about 44% of the constant’s equilibrium value, originating mainly from the large amplitude out-of-plane bending motion and that the temperature effects play a minor role.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Study of mass attenuation coefficients and effective atomic numbers of bismuth-ground granulated blast furnace slag concretes

Five samples of Bismuth-Ground granulated blast furnace slag (Bi-GGBFS) concretes were prepared using composition (0.6 cement + x Bi{sub 2}O{sub 3} + (0.4-x) GGBFS, x = 0.05, 0.10, 0.15, 0.20 and 0.25) by keeping constant water (W) cement (C) ratio. Mass attenuation coefficients (μ{sub m}) of these prepared samples were calculated using a computer program winXCOM at different gamma ray energies, whereas effective atomic numbers (Z{sub eff}) is calculated using mathematical formulas. The radiation shielding properties of Bi-GGBFS concrete has been compared with standard radiation shielding concretes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Multimode scanning X-ray diffraction microscopy for diamond anvil cell experiments

We have designed and implemented a new experimental system for fast mapping of crystal structures and other structural features of materials under high pressure at the High Pressure Collaborative Access Team, Sector 16 of the Advanced Photon Source. The system utilizes scanning X-ray diffraction microscopy (SXDM) and is optimized for use with diamond anvil cell devices. In SXDM, the X-ray diffraction (XRD) is collected in a forward scattering geometry from points on a two-dimensional grid by fly-scanning the sample with respect to a micro-focused X-ray beam. The recording of XRD is made during the continuous motion of the sample using a fast (millisecond) X-ray area detector in synchrony with the sample positioners, resulting in a highly efficient data collection for SXDM. A new computer program, X-ray Diffractive Imaging (XDI), has been developed with the SXDM system. The XDI program provides a graphical interface for constructing and displaying the SXDM images in several modes: (1) phase mapping based on structural information, (2) pressure visualization based on the equation of state, (3) microstructural features mapping based on peak shape parameters, and (4) grain size and preferred-orientation based on peak shape parameters. The XDI is a standalone program and can be generally used for displaying SXDM images. Two examples of iron and zirconium samples under high pressure are presented to demonstrate the applications of SXDM. Published under license by AIP Publishing.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Turning statistical physics models into materials design engines

Despite the success statistical physics has enjoyed at predicting the properties of materials for given parameters, the inverse problem, identifying which material parameters produce given, desired properties, is only beginning to be addressed. Recently, several methods have emerged across disciplines that draw upon optimization and simulation to create computer programs that tailor material responses to specified behaviors. However, so far the methods developed either involve black-box techniques, in which the optimizer operates without explicit knowledge of the material's configuration space, or require carefully tuned algorithms with applicability limited to a narrow subclass of materials. Here we introduce a formalism that can generate optimizers automatically by extending statistical mechanics into the realm of design. The strength of this approach lies in its capability to transform statistical models that describe materials into optimizers to tailor them. Here, by comparing against standard black-box optimization methods, we demonstrate how optimizers generated by this formalism can be faster and more effective, while remaining straightforward to implement. The scope of our approach includes possibilities for solving a variety of complex optimization and design problems concerning materials both in and out of equilibrium.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Laboratory earthquake forecasting: A machine learning competition

Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google’s ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data. The more than 4,500 participating teams created and shared more than 400 computer programs in openly accessible notebooks. Complementing the now well-known features of seismic data that map to fault criticality in the laboratory, the winning teams employed unexpected strategies based on rescaling failure times as a fraction of the seismic cycle and comparing input distribution of training and testing data. In addition to yielding scientific insights into fault processes in the laboratory and their relation with the evolution of the statistical properties of the associated seismic data, the competition serves as a pedagogical tool for teaching ML in geophysics. The approach may provide a model for other competitions in geosciences or other domains of study to help engage the ML community on problems of significance.

58 GEOSCIENCES↗
DOE OSTI2022

Multiphysics Analyses of the Bottom Components of the 3D Printed Transformational Challenge Reactor

This research presents multi-physics analyses on the bottom components of the Transformational Challenge Reactor (TCR) facility. These components include the bottom axial reflector and the steel exit cone. The bottom axial reflector is made of pure silicon carbide elements hosting helium cooling channels These elements are 3D printed and therefore can host any arbitrary shape of the helium cooling channels. The design of the bottom reflector considers the neutronics and thermo-fluid dynamics performances as well as the manufacturing process optimization. More precisely, the best design of the bottom reflector reduces neutron leakage by avoiding straight cylindrical helium channels that facilitate neutron leakage, minimizes the helium flow pressure drop, and reduces the number of 3D printed silicon carbide pieces. The exit cone steel structure collects the hot helium from the bottom fuel assemblies and channels the cold helium to the top of the fuel assemblies. The steel simultaneous contact with hot and cold helium flows sets a large thermal gradient. Different designs of the exit cone are proposed to reduce the steel equivalent stress from the helium thermal load. The multi-physics analyses have been performed using Ansys Fluent, Ansys Mechanical, STAR-CCM+, and Serpent computer programs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗