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At least 235 records · Page 13

Probabilistic Unsteady Aerodynamic Analysis

Probabilistic CFD design is needed because we are asked to do more with less. To cost effectively accomplish the design task, we need to formally quantify the effect of uncertainties (variables) in the design. Probabilistic design is one effective method to formally quantify the effect of uncertainties. Our objective is to establish a revolutionary new early design process, by developing non-deterministic physics-based probabilistic design tools, which will include all the life cycle processes. This work was concerned with the usefulness of parametric optimization method coupled with a Navier-Stokes analysis code for the aero-thermodynamic design of turbomachinery combustor liner. The interconnection between the CFD code and NESSUS codes will facilitate the coupling between the thermal profiles and structural design. We have developed new concepts for reducing the computational cost of unsteady, three-dimensional, compressible aerodynamic analyses for multistage turbomachinery flows. The flow was modeled by the three-dimensional Favre-Reynolds-averaged Navier-Stokes equations using the k-E turbulence closure, which was integrated using an implicit third-order upwind solver. The methodology developed in this work is expected to lead to the design optimization of turbomachinery blades.

Gorla, Rama S. R.↗

An analysis of the spatio-temporal resolution of the immersed boundary method with direct forcing

The immersed boundary method (IBM) with direct forcing is very popular in the simulation of rigid particulate flows. In the IBM, an interaction force is introduced at the interface between fluid and particle in order to approximate the no-slip boundary condition. The interaction force is calculated through dividing the velocity difference (or error) between fluid and particle at the interface by the time step. Here, a dynamic equation for the velocity difference is derived. Additionally, analyses on the dynamic equation provide a few new findings: (i) The interaction force is the solution of a least-squares error problem, with the direct implication that the Lagrangian marker distribution has no effect on the large scale flow structure once the distribution of Lagrangian markers become saturated along the interface (i.e., each marker remains properly correlated with all its neighbors); (ii) The Lagrangian volume-weight is a relaxation factor to control how fast the velocity error decays to the ideal value of zero; (iii) The optimal choice of the Lagrangian volume-weight is the largest value permissible by a stability condition. A comprehensive convergence analysis with regard to the spatial and temporal resolution is presented for the velocity error and also for the shear-stress and surface pressure. In three simple canonical problems, it is analytically and numerically shown that the IBM results converge to the theoretical solutions obtained with precise imposition of no-slip and no-penetration boundary conditions. It is observed that it is not necessary to match the Lagrangian marker volume-weight to that of the local Eulerian cell volume and in fact this matching leads to lower than optimal computational efficiency. However, it is found that extremely high Eulerian grid resolution and small time step have to be used to obtain high precision simulation results. Especially, the time step should be inversely proportional to the particle Reynolds number for low Reynolds number flows. For high frequency oscillation problems, the grid size needs to be reduced by a factor of the square root of the frequency, and the time step to be reduced by a factor of the frequency. The theoretical findings here can be used to alleviate the technical difficulties in simulating non-spherical particles by not requiring the Lagrangian marker distribution to match the Eulerian grids and also in the implementation of IBM on non-uniform Eulerian grids. The present work also provides simple practical guidance on the choice of temporal and spatial resolution so as to control the simulation error a priori.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Improving Miscibility of Polymer Donor and Polymer Acceptor by Reducing Chain Entanglement for Realizing 18.64 % Efficiency All Polymer Solar Cells

All-polymer solar cells have experienced rapid development in recent years by the emergence of polymerized small molecular acceptors (PSMAs). However, the strong chain entanglements of polymer donors (P D s) and polymer acceptors (P A s) decrease the miscibility of the resulting polymer mixtures, making it challenging to optimize the blend morphology. Herein, we designed three P A s, namely PBTPICm-BDD, PBTPICγ-BDD and PBTPICF-BDD, by smartly using a BDD unit as the polymerized unit to copolymerize with different Y-typed non-fullerene small molecular acceptors (NF-SMAs), thus achieving a certain degree of distortion and giving the polymer system enough internal space to reduce the entanglements of the polymer chains. Such effects increase the chances of the P D being interspersed into the acceptor material, which improve the solubility between the P D and P A . The PBTPICγ-BDD and PBTPICF-BDD displayed better miscibility with PBQx-TCl, leading to a well optimized morphology. As a result, high power conversion efficiencies (PCEs) of 17.50 % and 17.17 % were achieved for PBQx-TCl : PBTPICγ-BDD and PBQx-TCl : PBTPICF-BDD devices, respectively. In conclusion, with the addition of PYFT-o as the third component into PBQx-TCl : PBTPICγ-BDD blend to further extend the absorption spectral coverage and finely tune microstructures of the blend morphology, a remarkable PCE of 18.64 % was realized finally.

36 MATERIALS SCIENCE↗

XY vs X Mixer in Quantum Alternating Operator Ansatz for Optimization Problems with Constraints

Quantum Approximate Optimization Algorithm, further generalized as Quantum Alternating Operator Ansatz (QAOA), is a family of algorithms for combinatorial optimization problems. It is a leading candidate to run on emerging universal quantum computers to gain insight into quantum heuristics. In constrained optimization, penalties are often introduced so that the ground state of the cost Hamiltonian encodes the solution (a standard practice in quantum annealing). An alternative is to choose a mixing Hamiltonian such that the constraint corresponds to a constant of motion and the quantum evolution stays in the feasible subspace. Better performance of the algorithm is speculated due to a much smaller search space. We consider problems with a constant Hamming weight as the constraint. We also compare different methods of generating the generalized W-state, which serves as a natural initial state for the Hamming-weight constraint. Using graph-coloring as an example, we compare the performance of using XY model as a mixer that preserves the Hamming weight with the performance of adding a penalty term in the cost Hamiltonian.

quantum computing↗

Machine learning and atomic layer deposition: Predicting saturation times from reactor growth profiles using artificial neural networks

In this work, we explore the application of deep neural networks to the optimization of atomic layer deposition (ALD) processes. In particular, we focus on a one-shot optimization problem, where we try to predict the optimal dose time that leads to saturation everywhere in the reactor based on thickness values measured at different points of an ALD reactor after a single trial growth. In order to tackle this problem, we introduce a dataset designed to train neural networks to predict saturation times based on these inputs for a cross-flow ALD reactor. Here, we then explore the predictive ability of artificial neural networks of different depths and sizes using a separate testing dataset to evaluate their accuracies. The results obtained show that networks trained using stochastic gradient descent methods can accurately predict saturation times without requiring any additional information on the surface kinetics. This provides a viable approach to minimize the number of experiments required to optimize new ALD processes in a known reactor, and it highlights the way machine learning can be leveraged for thin film growth and manufacturing. While the datasets and training procedure depend on the reactor geometry, the trained neural networks provide a general surrogate model connecting thickness values and trial dose times with optimal saturation times that can be reused for different ALD processes within the same reactor.

36 MATERIALS SCIENCE↗

Top Stack Optimization for Cu 2 BaSn(S, Se) 4 Photovoltaic Cell Leads to Improved Device Power Conversion Efficiency beyond 6%

Earth-abundant and air-stable Cu 2 BaSnS 4-x Se x (CBTSSe) and related thin-film absorbers are regarded as prospective options to meet the increasing demand for low-cost solar cell deployment. Devices based on vacuum-deposited CBTSSe absorbers have achieved record power conversion efficiency (PCE) of 5.2 % based on a conventional device structure using CdS buffer and i-ZnO/ITO window layers, with open-circuit voltage (V OC ) posing the major bottleneck for improving solar cell performance. The current study demonstrates a >20 % improvement in V OC (from 0.62 V to 0.75 V) and corresponding enhancement in PCE (from 5.1 % to 6.2 % without anti-reflection coating; to 6.5 % with MgF 2 anti-reflection coating) for solution-deposited CBTSSe solar cells. This performance improvement is realized by introducing an alternative successive ionic layer adsorption and reaction (SILAR)-deposited Zn 1-x Cd x S buffer combined with sputtered Zn 1-x Mg x O/Al-doped ZnO window/top contact layer, which offer lower electron affinities relative to the conventional CdS/i-ZnO/ITO stack and better matching with the low electron affinity of CBTSSe. A combined experimental (temperature- and light intensity-dependent V OC measurements) and device simulation (SCAPS-1D) evaluation points to the importance of addressing relative band offsets for both the buffer and window layers relative to the absorber in mitigating interfacial recombination and optimizing CBTSSe solar cell performance.

25 ENERGY STORAGE↗

Bayesian Optimization Framework for Imperfect Data or Models

Conventional Bayesian optimization methods implicitly assume that the data and model being optimized are “perfect.” This assumption leads to inaccurate posterior probability distribution functions (PDFs) when applied to “imperfect” data or models. The new Bayesian optimization framework presented in this report provides a way to parameterize the effect of imperfections usually encountered in a prior PDF of generalized data or a model on the posterior PDF. The effects of imperfections are parameterized by a set of constraints imposed on the posterior expectation values of deviations between the data and the model and on their covariance matrix elements. A particular set of values for these constraints conveys an evaluator’s best estimate of the effect of imperfections on the corresponding posterior expectation values. When a prior PDF of generalized data is assumed to be normal, an expression for a posterior PDF satisfying an arbitrary set of constraints is derived analytically for linear models. An analogous iterative algorithm is given for nonlinear models. The corresponding posterior PDF should be used to estimate any posterior expectation values in the presence of imperfections parameterized by that set of constraints. A posterior PDF of a conventional Bayesian optimization method is recovered analytically when all evaluator-specified constraints are set to zero (i.e., in the absence of any imperfections). The analytical expressions derived in this report for normal PDFs and linear models were verified numerically by a Metropolis–Hastings Monte Carlo method. The methods presented herein could be applied to any kind of data or models, including differential cross-section data or integral benchmark experiments.

97 MATHEMATICS AND COMPUTING↗

Kinematics of Strong Discontinuities

Synthetic Aperture Radar (SAR) provides a detailed view of the Arctic ice cover. When processed with the RADARSAT Geophysical Processor System (RGPS), it provides estimates of sea ice motion and deformation over large regions of the Arctic for extended periods of time. The deformation is dominated by the appearance of linear kinematic features that have been associated with the presence of leads. The RGPS deformation products are based on the assumption that the displacement and velocity are smooth functions of the spatial coordinates. However, if the dominant deformation of multiyear ice results from the opening, closing and shearing of leads, then the displacement and velocity can be discontinuous. This presentation discusses the kinematics associated with strong discontinuities that describe possible jumps in displacement or velocity. Ice motion from SAR data are analyzed using this framework. It is assumed that RGPS cells deform due to the presence of a lead. The lead orientation is calculated to optimally account for the observed deformation. It is shown that almost all observed deformation can be represented by lead opening and shearing. The procedure used to reprocess motion data to account for leads will be described and applied to regions of the Beaufort Sea. The procedure not only provides a new view of ice deformation, it can be used to obtain information about the presence of leads for initialization and/or validation of numerical simulations.

Peterson, K.↗

The effect of lighting environment on task performance in buildings – A review

The effects of indoor environmental conditions on human health, satisfaction, and performance have been the focal point of research for decades. This paper reviews and summarizes the impact of lighting environment on task performance, specifically for the built environment audience. Existing studies included a variety of performance tests on cognitive performance and perception, visual acuity and reaction, memory, reasoning, and labor productivity. Illuminance, luminance ratio and correlated color temperature were found to affect performance in different ways, reflecting the impact of experimental techniques, conditions, performance evaluation methods used and data analysis methods. These were reviewed and categorized, with discussion on limitations related to sample size, modeling approach, carryover effects and other factors affecting individual differences in performance, with recommendations for future improvement. Although no universal conclusions can be made, in general, task performance seems to improve with higher illuminances, contrast ratios in the range of 7–11:1 (while always making sure that glare will not occur in the space) and higher correlated color temperature, while spectral tuning in the red or blue wavelengths has also shown positive effects. To obtain more generic evidence, future studies should be more consistent in terms of experimental procedures and overall light conditions, and also consider the effects of vertical illuminance, daylight provision/control, and outside views on task performance. Finally, studying performance with multi-factorial designs in a human-centered optimized manner (such as deploying variable lighting scenarios optimized for various tasks) can lead to deeper understanding of lighting effects on task performance, and ultimately to improved lighting design and operation in buildings overall.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Reducing measurement costs by recycling the Hessian in adaptive variational quantum algorithms

Abstract Adaptive protocols enable the construction of more efficient state preparation circuits in variational quantum algorithms (VQAs) by utilizing data obtained from the quantum processor during the execution of the algorithm. This idea originated with Adaptive Derivative-Assembled Problem-Tailored variational quantum eigensolver (ADAPT-VQE), an algorithm that iteratively grows the state preparation circuit operator by operator, with each new operator accompanied by a new variational parameter, and where all parameters acquired thus far are optimized in each iteration. In ADAPT-VQE and other adaptive VQAs that followed it, it has been shown that initializing parameters to their optimal values from the previous iteration speeds up convergence and avoids shallow local traps in the parameter landscape. However, no other data from the optimization performed at one iteration is carried over to the next. In this work, we propose an improved quasi-Newton optimization protocol specifically tailored to adaptive VQAs. The distinctive feature in our proposal is that approximate second derivatives of the cost function are recycled across iterations in addition to optimal parameter values. We implement a quasi-Newton optimizer where an approximation to the inverse Hessian matrix is continuously built and grown across the iterations of an adaptive VQA. The resulting algorithm has the flavor of a continuous optimization where the dimension of the search space is augmented when the gradient norm falls below a given threshold. We show that this inter-optimization exchange of second-order information leads the approximate Hessian in the state of the optimizer to be consistently closer to the exact Hessian. As a result, our method achieves a superlinear convergence rate even in situations where the typical implementation of a quasi-Newton optimizer converges only linearly. Our protocol decreases the measurement costs in implementing adaptive VQAs on quantum hardware as well as the runtime of their classical simulation.

Ramôa, Mafalda (ORCID:0000000302187801)↗

Sensitivity analysis, surrogate modeling, and optimization of pebble-bed reactors considering normal and accident conditions

This research provides a valuable tool that streamlines the optimization process while significantly increasing its accuracy. This study creates a robust framework for reactor design optimization by incorporating comprehensive modeling using the Comprehensive Reactor Analysis Bundle, or BlueCRAB, within the Multiphysics Object-Oriented Simulation Environment (MOOSE). BlueCRAB is the United States Nuclear Regulatory Commission's code suite for non-light water reactor analysis and includes the Griffin, Pronghorn, and Bison applications. This not only improves the efficiency of the optimization process but also enhances the reliability of the results. Such a tool is essential for advancing the state-of-the-art in pebble-bed reactor technology and is critical for achieving the goals of Generation IV reactors, which aim for safe, sustainable, and economically viable nuclear energy solutions. This work presents and applies this workflow on pebble-bed reactors while considering both normal and off-normal conditions. A representative gas-cooled pebble-bed reactor at equilibrium core conditions serves as the nominal design specification for normal operation and is based on previous research. The depressurized loss-of-forced-cooling accident is deployed for off-normal conditions in this work. After defining design-related parameters and quantities of interest regarding reactor safety and performance, this multiphysics model is sampled using the MOOSE stochastic tools module. The result is a comprehensive dataset of configurations, enabling sensitivity analysis and the generation of surrogate models. Subsequently, the dataset and surrogate models are employed in two optimization studies aimed at maximizing fuel utilization and economic profit while adhering to safety and operational constraints. Performing the optimization process with fuel utilization as the metric leads to an improvement of approximately 10%, compared to engineering-judgment-based nominal conditions. The optimization on economic profit leads to an estimated increase of ~300 million USD over the lifetime of the reactor.

97 MATHEMATICS AND COMPUTING↗

Experimental and Numerical Optimization of a High-Lift System to Improve Low-Speed Performance, Stability, and Control of an Arrow-Wing Supersonic Transport

An investigation was performed to evaluate leading-and trailing-edge flap deflections for optimal aerodynamic performance of a High-Speed Civil Transport concept during takeoff and approach-to-landing conditions. The configuration used for this study was designed by the Douglas Aircraft Company during the 1970's. A 0.1-scale model of this configuration was tested in the Langley 30- by 60-Foot Tunnel with both the original leading-edge flap system and a new leading-edge flap system, which was designed with modem computational flow analysis and optimization tools. Leading-and trailing-edge flap deflections were generated for the original and modified leading-edge flap systems with the computational flow analysis and optimization tools. Although wind tunnel data indicated improvements in aerodynamic performance for the analytically derived flap deflections for both leading-edge flap systems, perturbations of the analytically derived leading-edge flap deflections yielded significant additional improvements in aerodynamic performance. In addition to the aerodynamic performance optimization testing, stability and control data were also obtained. An evaluation of the crosswind landing capability of the aircraft configuration revealed that insufficient lateral control existed as a result of high levels of lateral stability. Deflection of the leading-and trailing-edge flaps improved the crosswind landing capability of the vehicle considerably; however, additional improvements are required.

Hahne, David E.↗

Integrating tactical planning, operational planning and scheduling using data-driven feasibility analysis

Supply chain operations and scheduling are well-studied problems in the literature. Although these problems are related, they are often solved sequentially. This uncoordinated approach usually leads to sub-optimal solutions. Here this paper proposes a methodology for integrating scheduling operations into the supply chain network, motivated by the available enterprise data, and feasibility analysis. Existing literature on integrated models for addressing the supply chain is reviewed and classified. Then the mathematical model formulation for each level is discussed, after which the proposed data-driven integrated framework is described. The methodology is tested on two different case studies of varying dimensions. For each case study, the solution solves a multi-objective problem with the overall aim of an optimal solution that is robust towards achieving optimal cost while keeping customer satisfaction in mind.

42 ENGINEERING↗

On the design of stable, consistent, and conservative high-order methods for multi-material hydrodynamics

Obtaining stable and high-order numerical solutions for multi-material hydrodynamics is an open challenge. Although slope limiters are widely used to maintain monotonicity near discontinuities, typical limiting procedures violate closure laws at the discrete level when applied to multi-material hydrodynamics equations. Due to this, the high-order expansions of quantities related by the closure laws are no longer consistent. The commonly observed symptom of this consistency-violation is that the numerical method fails to maintain constant pressure and velocity across material interfaces. This leads to sub-optimal convergence rates for smooth multi-material problems as well. Specialized limiting procedures that satisfy consistency while maintaining conservation need to be developed for such equations. A novel procedure that re-instates consistency into slope-limited high-order discretizations applied to the multi-material hydrodynamics equations is presented here. Using simple examples, it is demonstrated that the presented method satisfies closure laws at the discrete level, while maintaining conservative properties of the high-order method. Furthermore, this procedure involves a projection step which relies on the compact basis of the underlying spatial discretization, i.e. for discontinuous schemes (viz. DG and FV) the projection is local, and does not involve global matrix solves. Comparisons with conventional approaches emphasizes the necessity of the consistent closure-law preserving limiting approach, in order to maintain design order of accuracy for smooth multi-material problems.

36 MATERIALS SCIENCE↗

Enhanced CO 2 Methanation Activity of Sm 0.25 Ce 0.75 O 2-δ –Ni by Modulating the Chelating Agents-to-Metal Cation Ratio and Tuning Metal–Support Interactions

Highly active and selective CO 2 methanation catalysts are critical to CO 2 upgrading, synthetic natural gas production, and CO 2 emission reduction. Wet impregnation is widely used to synthesize oxide-supported metallic nanoparticles as the catalyst for CO 2 methanation. However, as the reagents cannot be homogeneously mixed at an atomic level, it is challenging to modulate the microstructure, crystal structure, chemical composition, and electronic structure of catalysts via wet impregnation. In this work, a scalable and straightforward catalyst fabrication approach has been designed and validated to produce Sm 0.25 Ce 0.75 O 2-δ -supported Ni (SDC–Ni) as the CO 2 methanation catalyst. By varying the chelating agents-to-total metal cations ratio (C/I ratio) during the catalyst synthesis, we can readily and simultaneously modulate the microstructure, metallic surface area, crystal structure, chemical composition, and electronic structure of SDC–Ni, consequently fine-tuning the oxide–support interactions and CO 2 methanation activity. The optimal C/I ratio (0.1) leads to an SDC–Ni catalyst that facilitates C–O bond cleavage and significantly improves CO 2 conversion at 250 °C. A CO 2 -to-CH 4 yield of >73% has been achieved at 250 °C. Furthermore, a stable operation of >1500 hours has been demonstrated, and no degradation is observed. Extensive characterizations were performed to fundamentally understand how to tune and enhance CO 2 methanation activity of SDC–Ni by modulating the C/I ratio. The correlation of physical, chemical, and catalytic properties of SDC–Ni with the C/I ratio is established and thoroughly elaborated in this work. This study could be applied to tune the oxide–support interactions of various catalysts for enhancing the catalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Origin of Large Effective Phonon Magnetic Moments in Monolayer MoS 2

Recent helicity-resolved magneto-Raman spectroscopy measurement demonstrates large effective phonon magnetic moments of ~2.5 μ B in monolayer MoS 2 , highlighting resonant excitation of bright excitons as a feasible route to activate Γ-point circularly polarized phonons in transition metal dichalcogenides. However, a microscopic picture of this intriguing phenomenon remains lacking. In this work, we show that an orbital transition between the split conduction bands (Δ 0 = 4 meV) of MoS 2 couples to the doubly degenerate E" phonon mode (Ω 0 = 33 meV), forming two hybridized states. Our phononic and electronic Raman scattering measurements capture these two states: (i) one with predominantly phonon contribution in the helicity-switched channels and (ii) one with primarily orbital contribution in the helicity-conserved channels. An orbital-phonon coupling model successfully reproduces the large effective magnetic moments of the circularly polarized phonons and explains their thermodynamic properties. Strikingly, the Raman mode from the orbital transition is superimposed on a strong quasi-elastic scattering background, indicating the presence of spin fluctuations. As a result, the electrons excited to the conduction bands through the exciton exhibit paramagnetic behavior although MoS 2 is generally considered as a nonmagnetic material. By depositing nanometer-thickness nickel thin films on monolayer MoS 2 , we tune the electronic structure so that the A exciton perfectly overlaps with the 633 nm laser. The optimization of resonance excitation leads to pronounced tunability of the orbital-phonon hybridized states. Our results generalize the orbital-phonon coupling model of effective phonon magnetic moments to material systems beyond the paramagnets and magnets.

2D transition metal dichalcogenide↗

Autonomous design of new chemical reactions using a variational autoencoder

Artificial intelligence based chemistry models are a promising method of exploring chemical reaction design spaces. However, training datasets based on experimental synthesis are typically reported only for the optimal synthesis reactions. This leads to an inherited bias in the model predictions. Therefore, robust datasets that span the entirety of the solution space are necessary to remove inherited bias and permit complete training of the space. In this study, an artificial intelligence model based on a Variational AutoEncoder (VAE) has been developed and investigated to synthetically generate continuous datasets. The approach involves sampling the latent space to generate new chemical reactions. This developed technique is demonstrated by generating over 7,000,000 new reactions from a training dataset containing only 7,000 reactions. The generated reactions include molecular species that are larger and more diverse than the training set.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Laser-initiated p – 11 B fusion reactions in petawatt high-repetition-rate laser facilities

Driving of the nuclear fusion reaction p + 11 B → 3α + 8.7 MeV under laboratory conditions by interaction between high-power laser pulses and matter has become a popular field of research, owing to its numerous potential applications: as an alternative to deuterium–tritium for fusion energy production, astrophysics studies, and alpha-particle generation for medical treatment. One possible scheme for laser-driven p– 11 B reactions is to direct a beam of laser-accelerated protons onto a boron (B) sample (the so-called “pitcher-catcher” scheme). This technique has been successfully implemented on large high-energy lasers, yielding hundreds of joules per shot at low repetition. We present here a complementary approach, exploiting the high repetition rate of the VEGA III petawatt laser at CLPU (Spain), aiming at accumulating results from many interactions at much lower energy, to provide better control of the parameters and the statistics of the measurements. Despite a moderate energy per pulse, our experiment allowed exploration of the laser-driven fusion process with tens (up to hundreds) of laser shots. The experiment provided a clear signature of the reactions involved and of the fusion products, accumulated over many shots, leading to an improved optimization of the diagnostics for experimental campaigns of this type. In this paper, we discuss the effectiveness of laser-driven p– 11 B fusion in the pitcher–catcher scheme, at a high repetition rate, addressing the challenges of this experimental scheme and highlighting its critical aspects. Our proposed methodology allows evaluation of the performance of this scheme for laser-driven alpha particle production and can be adapted to high-repetition-rate laser facilities with higher energy and intensity.

Alpha particles↗