Engineering topics
Jaffe, E.
Publications and source records attributed to Jaffe, E..
S&TR April-May 2023 (Corrosion Issue)
Corrosion damages and can ultimately destroy items from household appliances and personal vehicles to aircraft parts and reinforcing steel in bridges and roadways. As the article beginning on p. 4 describes, Lawrence Livermore researchers have developed capabilities for predicting the onset of corrosion in specific materials. Of particular interest to the Laboratory and its mission are impacts to weapon components stored in potentially corrosive environments.
Science & Technology Review (January/February 2023)
The vortex on this issue’s cover represents the sense of uncertainty experienced by scientists in the initial stages of exploring a new research path. The article beginning on p. 4 introduces research projects of higher technical risk conducted under Lawrence Livermore’s Disruptive Research (DR) Program, a component of the Laboratory Directed Research and Development Program. If successful, DR projects yield order-of-magnitude rewards. If DR projects fail to meet stated goals, they offer different rewards by shaping future research and investment
Science &Technology Review (October/November 2022)
Livermore researchers seek to understand the disease mechanism of amyotrophic lateral sclerosis (ALS) and, with that knowledge, identify therapies to cure this always-fatal disease. As the article beginning on p. 4 describes, the ALS research effort combines Laboratory core competencies in bioscience, engineering, computational modeling, and data science.
Science &Technology Review (September 2022)
Computer modeling is essential to scientific research. Models simulate natural phenomena to aid scientists in understanding their underlying principles. While the most complex models running on supercomputers may contain millions of lines of code and generate billions of data points, models never simulate reality perfectly. Experiments—in contrast—have been fundamental to the study of natural phenomena from science’s earliest days. However, some of today’s complex experiments generate too much data for the human mind to interpret.