LAB REPORT

Scientists have developed a new tool to simulate a nuclear device to change an asteroid’s path and prevent them from damaging Earth.

New research provides an innovative way to test the spread of energy from a nuclear device on the surface of an asteroid. The researchers hope to use the model to build on information from NASA’s 2022 DART mission. In this mission, the U.S. space agency deliberately rammed a spacecraft into the surface of the asteroid to divert it from its fixed orbital path.

Lawrence Livermore National Laboratory (LLNL) physicist Megan Burke-Sial said: “Although the probability of a large asteroid impact (hitting the Earth) in our lifetime is low, the possible consequences could be disastrous.”

Because nuclear devices have the highest energy-density ratio per unit mass due to their large mass, other scientists, including [those at] LLNL, have said that such technology could reduce the risks of asteroids. The new model developed by LLNL covers a wide range of physical factors, making the stimulation complex and computationally demanding. The researchers say the model simulates a number of complex factors, such as radiation and light penetration into the asteroid’s material.

Kathryn Mohror of Lawrence Livermore National Laboratory has been named the 2024 ACM SIGHPC Emerging Woman Leader In Technical Computing. Mohror is a Distinguished Member of the Technical Staff and Deputy Director of the Laboratory Directed Research and Development Program at LLNL.

Mohror is a leading researcher who has significantly contributed to a broad range of HPC I/O topics, programming models and tools for exascale computing. Since 2012, she has led the Scalable Checkpoint/Restart Framework (SCR) effort, an award-winning, production-level checkpoint/restart library that reduces I/O overhead by orders of magnitude. Within the Department of Energy (DOE) Exascale Computing Project (ECP), one of her contributions was co-leading the development of UnifyFS, a production, user-level file system designed for node-local storage on supercomputers.

Mohror is a dedicated and recognized leader within the HPC community.

Tropical forests play a crucial role in the global carbon cycle, accounting for over half of the world’s terrestrial carbon sink. However, climate change poses a significant threat to the carbon balance within these ecosystems.

A new study has revealed that the warming and drying of tropical forest soils could heighten the vulnerability of soil carbon. This is primarily due to the increased degradation of older carbon stores. 

The research was led by scientists from Lawrence Livermore National Laboratory (LLNL), in collaboration with researchers from Colorado State University and the Smithsonian Tropical Research Institute.

“These findings imply that both warming and drying, by accelerating the loss of older soil carbon or reducing the incorporation of fresh carbon inputs, will intensify soil carbon losses and negatively impact carbon storage in tropical forests under climate change,” said study lead author Karis McFarlane, a scientist at LLNL.

LLNL researchers have introduced an innovative new approach to 3D printing, called Microwave Volumetric Additive Manufacturing (MVAM), which uses microwave energy to cure materials — creating opportunities to 3D print with a broader range of materials.

In a recent paper published by Additive Manufacturing Letters, LLNL researchers describe the potential of microwave energy to penetrate a wider range of materials compared to light-based volumetric additive manufacturing (VAM). While VAM techniques like Computed Axial Lithography allow for rapid printing of complex 3D shapes in a single operation and eliminate the need for support structures, VAM relies on specific materials – primarily transparent and low-absorbing resins – which restricts the use of opaque or composite materials.

Compared to projected light, microwaves can reach deeper into materials – making them an ideal candidate for curing a variety of resins, including resins that are opaque or loaded with additives. This capability could significantly enhance the versatility of 3D printing, allowing for the creation of more complex, functional, and larger parts, according to LLNL research scientist Saptarshi Mukherjee, who co-led the paper with Lab materials chemist Johanna Schwartz.

Lattice structures, characterized by their complex patterns and hierarchical designs, offer immense potential across various industries, including automotive, aerospace, and biomedical engineering. With their outstanding high strength-to-weight ratio, customizability, and versatility, lattice structures enable the development of lightweight, durable components that can be precisely tailored to meet specific functional requirements.

However, the complexity of the structure and the vastness of the design space encompassed by lattice structures makes it challenging for traditional methods to thoroughly explore all possible configurations and pinpoint the optimal solution for the application. With each additional design variable, the possible configurations grow exponentially, making the design space intractable.

Lawrence Livermore engineers are looking to address these challenges by harnessing the power of machine learning (ML) and artificial intelligence (AI). Advanced computational tools powered by ML and AI have enabled LLNL researchers to accelerate and enhance the optimization of lattice structure designs significantly.

The research has set a new benchmark for intelligent design systems using computational modeling and ML algorithms. It also highlights AI’s pivotal role in designing lattice structures for a variety of applications.