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At least 1,585 records · Page 88

Broadband Ultrafast Dynamics of Refractory Metals: TiN and ZrN

Transition metal nitrides have recently gained attention in the fields of plasmonics, plasmon-enhanced photocatalysis, photothermal applications, and nonlinear optics because of their suitable optical properties, refractory nature, and large laser damage thresholds. Here we report comparative studies of the transient response of films of titanium nitride (TiN), zirconium nitride (ZrN), and gold (Au) under femtosecond excitation. Broadband transient optical characterization helps to adjudicate earlier, somewhat inconsistent reports regarding hot electron lifetimes based upon single wavelength measurements. These pump–probe experiments show sub-picosecond transient dynamics only within the epsilon-near-zero window of the refractory metals. The dynamics are dominated by photoinduced interband transitions resulting from ultrafast electron energy redistribution. The enhanced reflection modulation in the epsilon-near-zero window makes it possible to observe the ultrafast optical response of these films at low pump fluences. These results indicate that electron–phonon coupling in TiN and ZrN is 25–100 times greater than in Au. Strong electron–phonon coupling drives the sub-picosecond optical response and facilitates greater lattice heating compared to Au, making TiN and ZrN promising for photothermal applications. The spectral response and dynamics of TiN and ZrN are only weakly sensitive to pump fluence and pump excitation energy. However, the magnitude of the response is much greater at higher pump photon energies and higher fluences, reaching peak observed values of 15% in TiN and 50% in ZrN in the epsilon-near-zero window.

36 MATERIALS SCIENCE↗

Obtaining Large Columnar CdTe Grains and Long Lifetime on Nanocrystalline CdSe, MgZnO, or CdS Layers

CdTe solar cells have reached efficiencies comparable to multicrystalline silicon and produce electricity at costs competitive with traditional energy sources. Recent efficiency gains have come partly from shifting from the traditional CdS window layer to new materials such as CdSe and MgZnO, yet substantial headroom still exists to improve performance. Thin film technologies including Cu(In,Ga)Se2, perovskites, Cu2ZnSn(S,Se)4, and CdTe inherently have many grain boundaries that can form recombination centers and impede carrier transport; however, grain boundary engineering has been difficult and not practical. In this work, it is demonstrated that wide columnar grains reaching through the entire CdTe layer can be achieved by aggressive postdeposition CdTe recrystallization. This reduces the grain structure constraints imposed by nucleation on nanocrystalline window layers and enables diverse window layers to be selected for other properties critical for electro-optical applications. Computational simulations indicate that increasing grain size from 1 to 7 um can be equivalent to decreasing grain-boundary recombination velocity by three orders of magnitude. Here, large high-quality grains enable CdTe lifetimes exceeding 50 ns.

14 SOLAR ENERGY↗

Evaluation of a novel collimator for molecular breast tomosynthesis

Here, this study investigated a novel gamma camera for molecular breast tomosynthesis (MBT), which is a nuclear breast imaging method that uses limited angle tomography. The camera is equipped with a variable angle, slant-hole (VASH) collimator that allows the camera to remain close to the breast throughout the acquisition. The goal of this study was to evaluate the spatial resolution and count sensitivity of this camera and to compare contrast and contrast-to-noise ratio (CNR) with conventional planar imaging using an experimental breast phantom. Methods The VASH collimator mounts to a commercial gamma camera for breast imaging that uses a pixelated (3.2 mm), 15 × 20 cm NaI crystal. Spatial resolution was measured in planar images over a range of distances from the collimator (30-100 mm) and a range of slant angles (–25° to 25°) using 99m Tc line sources. Spatial resolution was also measured in reconstructed MBT images including in the depth dimension. The images were reconstructed from data acquired over the -25° to 25° angular range using an iterative algorithm adapted to the slant-hole geometry. Sensitivity was measured over the range of slant angles using a disk source. Measured spatial resolution and sensitivity were compared to theoretical values. Contrast and CNR were measured using a breast phantom containing spherical lesions (6.2 mm and 7.8 mm diameter) and positioned over a range of depths in the phantom. The MBT and planar methods had equal scan time, and the count density in the breast phantom data was similar to that in clinical nuclear breast imaging. The MBT method used an iterative reconstruction algorithm combined with a postreconstruction Metz filter. Results The measured spatial resolution in planar images agreed well with theoretical calculations over the range of distances and slant angles. The measured FWHM was 9.7 mm at 50 mm distance. In reconstructed MBT images, the spatial resolution in the depth dimension was approximately 2.2 mm greater than the other two dimensions due to the limited angle data. The measured count sensitivity agreed closely with theory over all slant angles when using a wide energy window. At 0° slant angle, measured sensitivity was 19.7 counts sec -1 μ Ci -1 with the open energy window and 11.2 counts sec -1 μ Ci -1 with a 20% wide photopeak window (126 to 154 keV). The measured CNR in the MBT images was significantly greater than in the planar images for all but the lowest CNR cases where the lesion detectability was extremely low for both MBT and planar. The 7.8 mm lesion at 37 mm depth was marginally detectable in the planar image but easily visible in the MBT image. The improved CNR with MBT was due to a large improvement in contrast, which out-weighed the increase in image noise. Conclusion The spatial resolution and count sensitivity measurements with the prototype MBT system matched theoretical calculations, and the measured CNR in breast phantom images was generally greater with the MBT system compared to conventional planar imaging. These results demonstrate the potential of the proposed MBT system to improve lesion detection in nuclear breast imaging.

60 APPLIED LIFE SCIENCES↗

The effects of electrically exploding gold bridgewires into inert and explosive powder beds

Abstract The particle velocity created in beds of both low-density inert sugar and explosive PETN as a function of distance from an exploding bridgewire was measured using optical velocimetry and a silvered PMMA window. As expected, more violent bridge-bursts (from a greater-stored-energy capacitive discharge unit) resulted in greater particle velocities and a better supported compaction wave in sugar. In all cases, ramp waves, not shocks, were observed in the inert sugar. Large window velocities were observed for very powerful bursts (up to 270 m/s), but bursts required for stochastic detonator operation conditions resulted in sugar/PMMA window velocities of only 8–10 m/s 0.85 mm from the bridge location. In contrast, after a distance of only 0.65 mm, a building shock wave was observed in PETN under both threshold and reliable firing conditions. Subsequently a hot-spot-driven shock-to-detonation (SDT) process was observed prior to full detonation. The measured buildup process accounts for $$\approx $$ ≈ 66% of the so-called excess transit time (ETT) between the observed and theoretical total function time for the particular exploding-bridge-wire (EBW) detonator studied. The remainder must occur in the powerful output pellet region. In contrast to a common understanding, the ETT is found to be a weak function of the discharge energy. Thus, the operation of the detonator after a bridge-burst energy-to-powder reaction transition process is found to be hot-spot-driven SDT in both the low- and high-density pellets.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Chemical preintercalation synthesis approach for the formation of new layered tungsten oxides

Tungsten oxide, WO 3 ·nH 2 O, is a unique layered oxide material that offers enhanced performance in electrochromic and energy storage applications. Herein, we report the formation of a new, never previously synthesized, Na-containing layered tungsten oxide phase, Na 0.20 WO 3 ·0.81H 2 O, using a chemical preintercalation approach. The structure and composition of this novel phase were investigated via microscopy, spectroscopy, and diffraction methods. Electrochemical cycling of Na 0.20 WO 3 ·0.81H 2 O electrodes revealed initial discharge capacities of 37.43 mAh g -1 , 480.8 mAh g -1 , and 253.2 mAh g -1 in aqueous H 2 SO 4 cells (potential window of - 0.2–0.8 V vs. Ag/AgCl), non-aqueous Li-ion cells (potential window of 0.1–4.0 V vs. Li/Li + ), and non-aqueous Na-ion cells (potential window of 0.1–4.0 V vs. Na/Na + ), respectively. Additionally, a reversible, pressure-induced color change from pale yellow to dark brown/black was observed for the Na 0.20 WO 3 ·0.81H 2 O sample when it was placed under pressures of 1000 psi or higher. Our results demonstrate the viability of chemical preintercalation synthesis approach to produce new oxide phases with interesting functional properties.

36 MATERIALS SCIENCE↗

Electron beam irradiation effects on bulk metals: a comparative study of polycrystalline versus single-crystalline structures

This study investigates the effects of electron beam (e-beam) irradiation on the mechanical and structural properties of eight bulk metallic samples, comprising both polycrystalline (PC) and single-crystalline (SC) forms of Ni, Cr, V, and Ti. These metals were evaluated as potential candidates for beam exit windows in high-power (MW-class) particle accelerators. The primary objective is to identify metals capable of withstanding the conditions of high-power/MW-class e-beam accelerators and serve effectively as exit windows. Selection criteria were based on each metal’s intrinsic properties, power dissipation capability, and irradiation-induced changes in mechanical behavior, including hardness, elastic modulus, and defect density. Comprehensive characterization was conducted using field-emission scanning electron microscopy (FESEM), atomic force microscopy (AFM), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDS), X-ray diffraction (XRD), high-resolution transmission electron microscopy (HRTEM), selected area electron diffraction (SAED), and nanoindentation, performed both before and after exposure to a ~¿66 kGy dose from a 10 MeV e-beam accelerator. Results revealed that e-beam irradiation induced hardening in PC Ni, whereas PC Ti, commonly used in beam exit windows, exhibited softening. The observed softening in PC Ti is attributed to grain coarsening, elongation, and the formation of twins and twin boundaries, in contrast to the smaller, compressed grains in the pristine (Pr) PC Ti samples, consistent with the Hall–Petch relationship. The stresses due to twinning are small and insignificant in influencing the overall hardening of the PC Ti irradiated sample when compared to the stresses due to the dislocation density. Conversely, SC Ti samples exhibited irradiation-induced hardening. The SC Ti irradiated samples developed additional irradiation-induced modifications in crystallographic texture of (100), (101), (110), (200), (112), (004), and (211) as evidenced from the XRD results, which could probably explain the hardening effect that is caused by irradiation.

36 MATERIALS SCIENCE↗

High purity Mn 5 O 8 nanoparticles with a high overpotential to gas evolution reactions for high voltage aqueous sodium-ion electrochemical storage

Here, developing electrodes with high specific energy by using inexpensive manganese oxides is of great importance for aqueous electrochemical energy storage (EES) using non-Li charge carriers such as Na-or K-ions. However, the energy density of aqueous EES devices is generally limited by their narrow thermodynamic potential window (~1.23 V). In this paper, the synthesis of high purity layered Mn 5 O 8 nanoparticles through solid state thermal treatment of Mn3O4 spinel nanoparticles, resulting in a chemical formula of [Mn 2 2+ ] [Mn 3 4+ O 8 2– ], evidenced by Rietveld refinement of synchrotron- based X-ray diffraction, has been reported. The electro-kinetic analyses obtained from cyclic voltammetry measurements in half-cells have demonstrated that Mn5O8 electrode has a large overpotential (~ 0.6 V) towards gas evolution reactions, resulting in a stable potential window of 2.5 V in an aqueous electrolyte in half-cell measurements. Symmetric full-cells fabricated using Mn5O8 electrodes can be operated within a stable 3.0 V potential window for 5000 galvanostatic cycles, exhibiting a stable electrode capacity of about 103 mAh/g at a C-rate of 95 with nearly 100% coulombic efficiency and 96% energy efficiency.

25 ENERGY STORAGE↗

High-Resolution Mass Spectrometry Quantification: Impact of Differences in Data Processing of Centroid and Continuum Data

High-resolution mass spectrometry (HRMS) in full scan mode acquires all ions present in the sample of interest offering a lot of qualitative information. This, in combination with the improved performance of the new generation HRMS systems, triggers more (bio) analysts to switch from triple quad MS systems to HRMS for quantitative analysis. Quantitative processing of HRMS data is performed based on narrow mass extraction windows rather than on nominal mass product ion chromatograms (SRM or MRM). Optimal processing of HRMS data requires different considerations and software tools and can have an impact on data processing and final results. The selection of centroid versus continuum/profile data for processing, selection of the optimal narrow mass extraction window, using theoretical versus measured accurate mass for the extraction of the ion chromatograms as well as differences in calculations and data handling residing in the different vendor software packages are tackled in the presented manuscript. These differences are illustrated on HRMS data acquired for the same plasma samples on three different platforms, i.e., a Sciex QToF, a Waters QToF, and a Thermo Orbitrap system, and processed in four different software packages, i.e., Sciex Analyst® TF, Waters Masslynx, Waters Unifi, and Thermo Xcalibur. The impact of these differences on quantitative HRMS performance was evaluated on calibration curves of eight small molecule compounds in plasma using four different ways of processing. Simple guidelines are provided for the selection of an optimal mass extraction window for continuum and centroided data. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Seasonal Disorder in Urban Traffic Patterns: A Low Rank Analysis

This article proposes several advances to sparse nonnegative matrix factorization (SNMF) as a way to identify large-scale patterns in urban traffic data. The input to our model is traffic counts organized by time and location. Nonnegative matrix factorization additively decomposes this information, organized as a matrix, into a linear sum of temporal signatures. Penalty terms encourage this factorization to concentrate on only a few temporal signatures, with weights which are not too large. Our interest here is to quantify and compare the regularity of traffic behavior, particularly across different broad temporal windows. In addition to the rank and error, we adapt a measure introduced by Hoyer to quantify sparsity in the representation. Combining these, we construct several curves which quantify error as a function of rank (the number of possible signatures) and sparsity; as rank goes up and sparsity goes down, the approximation can be better and the error should decreases. Plots of several such curves corresponding to different time windows leads to a way to compare disorder/order at different time scalewindows. In this paper, we apply our algorithms and procedures to study a taxi traffic dataset from New York City. In this dataset, we find weekly periodicity in the signatures, which allows us an extra framework for identifying outliers as significant deviations from weekly medians. We then apply our seasonal disorder analysis to the New York City traffic data and seasonal (spring, summer, winter, fall) time windows. We do find seasonal differences in traffic order.

97 MATHEMATICS AND COMPUTING↗

A Model of Grain Boundary Complexion Transitions and Grain Growth in Yttria-Doped Alumina

In this work, we present a physically-parameterized microstructure evolution model for the Yttria-doped alumina system. Yttria-doped alumina is a well-known ceramic system which undergoes first-order phase-like transitions at grain boundaries, which can radically alter interface properties. The change in interfacial properties in turn can radically change microstructure outcomes during processing, including the induction of abnormal grain growth modes. In this work, we develop a simulation that evolves alumina microstructure as a function of yttria concentration and temperature. In the window studied, we achieve strong agreement with reviewed experimental results in identifying the windows for large grains, small grains, abnormal grain growth, and complexion transition kinetics. We then apply the model to study and demonstrate how the possible inclusion of second-phase particles or uneven solute distribution profiles will impact microstructure evolution. It is found that particles do not significantly affect abnormal grain growth in the window studied (but do lead to reduced grain size through pinning effects). It is found that even modest amounts of solute inhomogeneity will result in substantial changes in microstructure outcomes, frequently leading to clusters of abnormal grains. This model largely corroborates the expectations and hypotheses made from recent experimental studies in oxide-doped alumina systems. Further, it is found that there exists a peak transition fraction for the system at which abnormal grain size tends to be maximized.

Grain Growth, Grain Boundary Complexion, abnormal ↗

National energy savings potential of cellular shades: A measurement and simulation study

Windows are major contributors to energy demand in residential homes because of their inferior thermal resistance compared with the opaque envelope, and sometimes from unwanted solar heat gain. Window attachments can help mitigate the energy demand by controlling the solar heat gains and enhancing window thermal resistance. Cellular shades have the potential of superior thermal performance compared with generic shades because of its honeycomb structure. Here, in this study, the team analyzed the energy savings potential of cellular shades in residential homes via experimental testing for two heating seasons and energy simulations. Five shading devices—three single-cell and two double cellular/cell-in-cell shades—were used to compare the performance with generic horizontal venetian blinds using two nearly identical side-by-side rooms in a residential home. The experimental testing showed daily heating energy savings in the range of 17%–36% compared with the case without shades. The experimental testing data also exhibited improvements in thermal comfort when using cellular shades. Additionally, energy simulations were performed to evaluate the energy savings potential of the cellular shades using a residential prototype home, which demonstrated energy savings up to 9 kWh/m2/year in cold climates. The total site energy savings for heating and cooling from cellular shades was up to ~9% for the home with a heat pump and up to ~15% for a home with a gas furnace compared with cases without any shading devices. The energy savings at a national scale were up to 14.6 TWh assuming a 20% penetration rate in residential homes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Divide and conquer: Learning chaotic dynamical systems with multistep penalty neural ordinary differential equations

Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering. Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems. However, classical techniques used for NODE training are ineffective for learning chaotic dynamical systems. In this work, we propose a novel NODE-training approach that allows for robust learning of chaotic dynamical systems. Here, our method addresses the challenges of non-convexity and exploding gradients associated with underlying chaotic dynamics. Training data trajectories from such systems are split into multiple, non-overlapping time windows. In addition to the deviation from the training data, the optimization loss term further penalizes the discontinuities of the predicted trajectory between the time windows. The window size is selected based on the fastest Lyapunov time scale of the system. Multi-step penalty(MP) method is first demonstrated on Lorenz equation, to illustrate how it improves the loss landscape and thereby accelerates the optimization convergence. MP method can optimize chaotic systems in a manner similar to least-squares shadowing with significantly lower computational costs. Our proposed algorithm, denoted the Multistep Penalty NODE, is applied to chaotic systems such as the Kuramoto-Sivashinsky equation, the two-dimensional Kolmogorov flow, and ERA5 reanalysis data for the atmosphere. It is observed that MP-NODE provide viable performance for such chaotic systems, not only for short-term trajectory predictions but also for invariant statistics that are hallmarks of the chaotic nature of these dynamics.

Chaotic dynamical systems↗

RED-BL: Evaluating dynamic workload relocation for data center networks

In this study, we present RED-BL (Relocate Energy Demand to Better Locations), a framework to minimize the electricity cost for operating data center networks over consecutive intervals of fixed duration. Within each interval, RED-BL provides a mapping of workload to a set of geographically distributed data centers. To this end, RED-BL uses the geographical and temporal variations in electricity prices as exhibited by electrical energy markets. In addition, we incorporate the transition costs associated with a change in workload mapping from one interval to the next, over a planning window comprising multiple such intervals. This results in a sequence of workload mappings that is optimal over the entire planning window, even though the workload mapping in a given interval may not be locally optimal. Our evaluation of RED-BL uses electricity prices from the US markets and workload traces from live Internet applications with millions of users. We find that RED-BL can reduce the electric bill by as much as 45% compared to the case when the workload is uniformly distributed. When compared to existing workload relocation solutions, for a wide range of data center deployment sizes, RED-BL achieves electricity cost savings that are 8.28% higher, on average. This seemingly modest reduction can save millions of dollars for the operators. The cost of this saving is an inexpensive computation at the start of each planning window.

97 MATHEMATICS AND COMPUTING↗

Local reduced-order modeling for electrostatic plasmas by physics-informed solution manifold decomposition

Despite advancements in high-performance computing and modern numerical algorithms, computational cost remains prohibitive for multi-query kinetic plasma simulations. Here, in this work, we develop data-driven reduced-order models (ROMs) for collisionless electrostatic plasma dynamics, based on the kinetic Vlasov-Poisson equation. Our ROM approach projects the equation onto a linear subspace defined by the proper orthogonal decomposition (POD) modes. We introduce an efficient tensorial method to update the nonlinear term using a precomputed third-order tensor. We capture multiscale behavior with a minimal number of POD modes by decomposing the solution manifold into multiple time windows and creating temporally local ROMs. We consider two strategies for decomposition: one based on the physical time and the other based on the electric field energy. Applied to the 1D1V Vlasov–Poisson simulations, that is, prescribed E-field, Landau damping, and two-stream instability, we demonstrate that our ROMs accurately capture the total energy of the system both for parametric and time extrapolation cases. The temporally local ROMs are more efficient and accurate than the single ROM. In addition, in the two-stream instability case, we show that the energy-windowing reduced-order model (EW-ROM) is more efficient and accurate than the time-windowing reduced-order model (TW-ROM). With the tensorial approach, EW-ROM solves the equation approximately 90 times faster than Eulerian simulations while maintaining a maximum relative error of 7.5% for the training data and 11% for the testing data.

Electrostatic plasmas↗

DNS and LES of turbulent flow in a closed channel featuring a pattern of hemispherical roughness elements

Three mesh adaptivity algorithms were developed to facilitate and expedite the use of the CADIS and FW-CADIS hybrid Monte Carlo/deterministic techniques in accurate full-scale neutronics simulations of fusion energy systems with immense sizes and complicated geometries. First, a macromaterial approach enhances the fidelity of the deterministic models without changing the mesh. Second, a deterministic mesh refinement algorithm generates meshes that capture as much geometric detail as possible without exceeding a specified maximum number of mesh elements. Finally, a weight window coarsening algorithm decouples the weight window mesh and energy bins from the mesh and energy group structure of the deterministic calculations in order to remove the memory constraint of the weight window map from the deterministic mesh resolution. The three algorithms were used to enhance an FW-CADIS calculation of the prompt dose rate throughout the ITER experimental facility and resulted in a 23.3% increase in the number of mesh tally elements in which the dose rates were calculated in a 10-day Monte Carlo calculation. Additionally, because of the significant increase in the efficiency of FW-CADIS simulations, the three algorithms enabled this difficult calculation to be accurately solved on a regular computer cluster, eliminating the need for a world-class super computer.

Nuclear Science & Technology↗

Accuracy and Consistency of Respiratory Gating in Abdominal Cancer Patients

Purpose: To evaluate respiratory gating accuracy and intrafractional consistency for abdominal cancer patients treated with respiratory gated treatment on a regular linear accelerator system. Methods and Materials: Twelve abdominal patients implanted with fiducials were treated with amplitude-based respiratory-gated radiation therapy. On the basis of daily orthogonal fluoroscopy, the operator readjusted the couch position and gating window such that the fiducial was within a setup margin (fiducial-planning target volume [f-PTV]) when RPM indicated “beam-ON.” Fifty-five pre- and post-treatment fluoroscopic movie pairs with synchronized respiratory gating signal were recorded. Fiducial motion traces were extracted from the fluoroscopic movies using a template matching algorithm and correlated with f-PTV by registering the digitally reconstructed radiographs with the fluoroscopic movies. Treatment was determined to be “accurate” if 50% of the fiducial area stayed within f-PTV while beam-ON. For movie pairs that lost gating accuracy, a MATLAB program was used to assess whether the gating window was optimized, the external-internal correlation (EIC) changed, or the patient moved between movies. A series of safety margins from 0.5 mm to 3 mm was added to f-PTV for reassessing gating accuracy. Results: A decrease in gating accuracy was observed in 44% of movie pairs from daily fluoroscopic movies of 12 abdominal patients. Three main causes for inaccurate gating were identified as change of global EIC over time (∼43%), suboptimal gating setup (∼37%), and imperfect EIC within movie (∼13%). Conclusions: Inconsistent respiratory gating accuracy may occur within 1 treatment session even with a daily adjusted gating window. To improve or maintain gating accuracy during treatment, we suggest using at least a 2.5-mm safety margin to account for gating and setup uncertainties.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Dynamic calibration of differential equations using machine learning, with application to turbulence models

We present a methodology for calibration of parametric ordinary and partial differential equation models, using off-the-shelf software for back-propagation in Neural Networks (NN). As a prototypical example, we consider calibration of a Reynolds-averaged Navier-Stokes (RANS) turbulence closure model, against ground truth data from direct numerical simulations (DNS) of two different turbulent flows. Numerical time integration is represented as a custom NN, where only the RANS model parameters are trainable. A loss function is defined to quantify the mismatch between the NN prediction and the ground truth over a predefined, finite time integration window. This loss function is then minimized using a gradient descent method utilizing the back-propagation algorithm. Furthermore, this dynamic approach to training is to be contrasted with a static approach, wherein a least square regression estimate for parameters is obtained in the limit of an infinitesimal time integration window. In a first test of static and dynamic approaches against ground truth data generated by the model, the former proves to be significantly faster and more accurate than the latter at recovering the parameters. When both calibration approaches are tested against DNS data, for which it is known that the model cannot achieve a perfect fit, the static approach yields a good prediction only for short times, while the dynamic approach results in physical and stable predictions over the entire integration window. After optimization of the dynamic approach for time step, spatial resolution, stability, and physics-based constraints, we obtain a 50% improvement of outcomes over those obtained from the existing, manually calibrated set of parameters, demonstrating the merits of this systematic and automated procedure.

97 MATHEMATICS AND COMPUTING↗

Insights into radiation resistance of titanium alloys from displacement cascade simulations

Radiation damage in beam window materials limits the use of high power proton beams in high energy physics research. The alloy Ti-6Al-4V is presently used as a beam window material but a prospective alternative, Ti-15V-3Cr-3Sn-3Al has been proposed. Since both these alloys contain dual phases at room temperature, we compare the radiation damage in the α and ß-phases of these two materials via primary knock-on atom (PKA) cascade simulations in the 10-40keV energy range. At PKA energies 30 and 40keV, the number of Frenkel pairs in the ballistic stage is higher in the ß-phase of Ti-6Al-4V than that in the ß-phase of Ti-15V-3Cr-3Sn-3Al almost by a factor of 2. The α-phase, of both these alloys, by far outperforms the ß-phases of the two alloys, both in terms of damage during the ballistic stage and in terms of the surviving defects. The average displacement threshold energy (E d ) in the α-phase of both alloys was found to be 66eV while that in the ß-phase of Ti-15-3 was 55 eV and in the ß-phase of Ti-6-4 was 46. Further, while the number of surviving defects is almost equal in both alloys, the vacancy and interstitial clustering mechanisms differ notably, which can impact the degree of radiation hardening and loss of ductility. Our simulations show larger vacancy and interstitial clusters form in Ti-6Al-4V as compared to that in Ti-15-3-3-3 alloy. These results indicate that Ti-15-3-3-3 alloys may be a promising candidate for next generation beam window material with a higher radiation tolerance than the existing Ti-6-4 alloy.

36 MATERIALS SCIENCE↗