Search NASA⌕ Search

DOE OSTI · 2999041

Optimizing ablator thickness for laser shock experiments

Abstract

In laser shock experiments, a well-defined, flat-top shock wave at the ablator/sample interface is important for accurately probing material response under uniaxial strain compression. However, the relationship between the ablator thickness and the resulting shock wave characteristics is insufficiently understood, limiting the ability to design optimal experiments. To address this need, we conducted a systematic experimental study using a 100 J laser to shock-compress polyimide ablators to peak stresses ranging from 20.4 to 111.6 GPa. Laser interferometry diagnostics measured the transmitted wave profiles at the ablator/sample interface, consistently showing a single jump followed by a constant peak state before the arrival of release waves. Here, by analyzing shock transit time, flat-top duration, and stress, our results establish a framework for selecting ablator thickness to maximize the flat-top duration, improving the precision and reproducibility of laser shock experiments.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rigg, P. A. [Washington State University, Lemont, IL (United States)] (ORCID:0000000227131321), Renganathan, P. [Washington State University, Lemont, IL (United States)] (ORCID:000000015012806X), Sinclair, N. W. [Washington State University, Lemont, IL (United States)] (ORCID:0000000196898983), Toyoda, Y. [Washington State University, Lemont, IL (United States)] (ORCID:0000000296155925), Wang, X. [Washington State University, Lemont, IL (United States)] (ORCID:0000000264902666), Gupta, Y. M. [Washington State University, Lemont, IL (United States); Washington State University, Pullman, WA (United States)] (ORCID:0000000226693015). 2025-10-21. Optimizing ablator thickness for laser shock experiments. https://doi.org/10.1063/5.0286493

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Throughput Optimization of Molybdenum Carbide Nanoparticle Catalysts in a Continuous Flow Reactor Using Design of Experiments

Transition metal carbides (TMCs) have attracted significant attention because of their applications toward a wide range of catalytic transformations. However, the practicality of their synthesis is still limited because of the harsh conditions in which most TMCs are prepared. Recently, a solution-phase synthesis of phase-pure a-MoC1-x nanoparticles was presented. While this synthetic route yielded nanoparticles with exceptional catalytic performance, the reaction parameter space was not explored, and catalyst throughput was not optimized for scale-up. Continuous flow platforms coupled with statistical design of experiments (DoE) can provide a powerful method for understanding the reaction parameter space for optimizations. Here, we demonstrate the use of statistical DoE in tandem with response surface methodology for a parametric screening analysis to optimize the throughput of a MoC1-x nanoparticle synthesis utilizing a millifluidic flow reactor. A full factorial design was implemented to evaluate four input variables (reaction temperature, flow rate, solvent fraction of oleylamine, and precursor concentration) that carry statistically significant effects on three responses (throughput, residence time, and isolated yield). A Doehlert matrix was implemented to investigate each significant variable at a higher number of levels to optimize throughput. Our results give a nonintuitive set of experimental conditions that resulted in an optimized throughput of 2.2 g h-1. This translates to a 50-fold increase in throughput compared to the previously reported batch method. The catalytic performance of the MoC1-x nanoparticles produced under optimized throughput was demonstrated in the CO2 hydrogenation reaction. This DoE screening analysis and throughput optimization of MoC1-x synthesis open the door to an increased feasibility for scale-up.

design of experiments↗

Establishing Fault Tolerance for a Class of Systems by Experiment

A long-standing problem in system verification is establishing fault tolerance at the ultra-high level by experiment. It is considered impossible because of system complexity and the enormous number of trials needed. This paper considers the problem for a class of digital systems that use redundancy to achieve reliability. The class is the systems that operate for a period of time without maintenance followed by a maintenance check that replaces components identified as faulty. The paper considers simulating a natural life test where a natural life test observes a number of operating periods. If the system does not fail during the test, it can be said to have a certain reliability at a certain confidence level. The approach in this paper is to make the simulated life test more efficient while maintaining realism by integrating structural arguments, information on fault occurrence, and fault injection in the lab. The major result of this paper is constructing a global fault model using the failure rate of the components and proving theorems about the model that tell how many, what kind, when, and where to inject faults. A simple example illustrates applying the theorems.

design of experiments↗

Evaluation of Response Surface Experiment Designs for Distributed Propulsion Aircraft Aero-Propulsive Modeling

Modern distributed hybrid and electric propulsion aircraft, including vertical, short, and conventional takeoff and landing configurations, exhibit significant aero-propulsive complexity and a large number of interacting test factors. This paper presents the development and evaluation of experiment designs for aero-propulsive characterization of distributed propulsion aircraft. Five different foundational response surface designs are evaluated to inform the development of two sequential design approaches tailored to complex aircraft aerodynamic characterization experiments. The first approach, which builds on sequential face-centered central composite designs, has been used previously to develop aero-propulsive models for complex aircraft using wind tunnel testing. The second approach is a new design strategy leveraging a regular I-optimal and nested I-optimal design that was developed for this study. The two sequential design strategies are compared for experiments with a large number of test factors using pre-experiment design evaluation metrics, as well as modeling results obtained from simulated wind tunnel data for the NASA LA-8 aircraft. The design evaluation metrics show that the sequential I-optimal base design has higher statistical power, lower correlation among candidate regressors, lower prediction variance, and more precise parameter estimates. The simulated wind tunnel experiments conducted using each design reveal that the sequential I-optimal base design has better predictive capability with fewer test points. The experiment design and evaluation procedures are described in detail to inform future aerodynamic characterization experiments for complex aircraft.

design of experiments↗