Roger Lowe-Webb

National Ignition Facility system alignment

The National Ignition Facility (NIF) is the worlds largest optical instrument, comprising 192 37 cm square beams, each generating up to 9.6 kJ of 351 nm laser light in a 20 ns beam precisely tailored in time and spectrum. The Facility houses a massive (10 m diameter) target chamber within which the beams converge onto an ∼1 cm size target for the purpose of creating the conditions needed for deuterium/tritium nuclear fusion in a laboratory setting. A formidable challenge was building NIF to the precise requirements for beam propagation, commissioning the beam lines, and engineering systems to reliably and safely align 192 beams within the confines of a multihour shot cycle. Designing the facility to minimize drift and vibration, placing the optical components in their design locations, commissioning beam alignment, and performing precise system alignment are the key alignment accomplishments over the decade of work described herein. The design and positioning phases placed more than 3000 large (2.5 m×2 m×1 m) line-replaceable optics assemblies to within ±1 mm of design requirement. The commissioning and alignment phases validated clear apertures (no clipping) for all beam lines, and demonstrated automated laser alignment within 10 min and alignment to target chamber center within 44 min. Pointing validation system shots to flat gold-plated x-ray emitting targets showed NIF met its design requirement of ±50 μm rms beam pointing to target chamber. Finally, this paper describes the major alignment challenges faced by the NIF Project from inception to present, and how these challenges were met and solved by the NIF design and commissioning teams.

The commissioning of the advanced radiographic capability laser system: experimental and modeling results at the main laser output

The National Ignition Facility (NIF) at Lawrence Livermore National Laboratory is the first of a kind megajoule-class laser with 192 beams capable of delivering over 1.8 MJ and 500TW of 351nm light [1], [2]. It has been commissioned and operated since 2009 to support a wide range of missions including the study of inertial confinement fusion, high energy density physics, material science, and laboratory astrophysics. In order to advance our understanding, and enable short-pulse multi-frame radiographic experiments of dense cores of cold material, the generation of very hard x-rays above 50 keV is necessary. X-rays with such characteristics can be efficiently generated with high intensity laser pulses above 1017 W/cm² [3]. The Advanced Radiographic Capability (ARC) [4] which is currently being commissioned on the NIF will provide eight, 1 ps to 50 ps, adjustable pulses with up to 1.7 kJ each to create x-ray point sources enabling dynamic, multi-frame x-ray backlighting. This paper will provide an overview of the ARC system and report on the laser performance tests conducted with a stretched-pulse up to the main laser output and their comparison with the results of our laser propagation codes.

Beam and target alignment at the National Ignition Facility using the Target Alignment Sensor (TAS)

The requirements for beam and target alignment for successful ignition experiments on the National Ignition Facility (NIF) are stringent: the average of beams to the target must be within 25 μm. Beam and target alignment are achieved with the Target Alignment Sensor (TAS). The TAS is a precision optical device that is inserted into target chamber center to facilitate both beam and target alignment. It incorporates two camera views (upper/lower and side) mounted on each of two stage assemblies (jaws) to view and align the target. It also incorporates a large mirror on each of the two assemblies to reflect the alignment beams onto the upper/lower cameras for beam alignment. The TAS is located in the chamber using reference features by viewing it with two external telescope views. The two jaws are adjusted in elevation to match the desired beam and target alignment locations. For some shot setups, a sequence of TAS positions is required to achieve the full setup and alignment. In this paper we describe the TAS, the characterization of the TAS coordinates for beam and target alignment, and summarize pointing shots that demonstrate the accuracy of beam-target alignment.

Near Field Intensity Trends of Main Laser Alignment Images in the National Ignition Facility (NIF)

The National Ignition Facility (NIF) utilizes 192 high-energy laser beams focused with enough power and precision on a hydrogen-filled spherical, cryogenic target to potentially initiate a fusion reaction. NIF has been operational for six years and during that time, thousands of successful laser firings or shots have been executed. Critical instrument measurements and camera images are carefully recorded for each shot. The result is a massive and complex database or ‘big data’ archive that can be used to investigate the state of the laser system at any point in its history or to locate and track trends in the laser operation over time. In this study, the optical light throughput for more than 1600 NIF shots for each of the 192 main laser beams and 48 quads was measured over a three year period from January 2009 to October 2012. The purpose was to verify that the variation in the transmission of light through the optics performed within design expectations during this time period. Differences between average or integrated intensity from images recorded by the input sensor package (ISP) and by the output sensor package (OSP) in the NIF beam-line were examined. A metric is described for quantifying changes in the integrated intensity measurements. Changes in light transmission from the NIF main laser over the three year time-frame are presented.

Recent advances in automatic alignment system for the National Ignition Facility

The automatic alignment system for the National Ignition Facility (NIF) is a large-scale parallel system that directs all 192 laser beams along the 300-m optical path to a 50-micron focus at target chamber in less than 50 minutes. The system automatically commands 9,000 stepping motors to adjust mirrors and other optics based upon images acquired from high-resolution digital cameras viewing beams at various locations. Forty-five control loops per beamline request image processing services running on a LINUX cluster to analyze these images of the beams and references, and automatically steer the beams toward the target. This paper discusses the upgrades to the NIF automatic alignment system to handle new alignment needs and evolving requirements as related to various types of experiments performed. As NIF becomes a continuously-operated system and more experiments are performed, performance monitoring is increasingly important for maintenance and commissioning work. Data, collected during operations, is analyzed for tuning of the laser and targeting maintenance work. Handling evolving alignment and maintenance needs is expected for the planned 30-year operational life of NIF.

Image processing for the automatic alignment at the national ignition facility

The Automatic Alignment system in the National Ignition Facility is responsible for aligning 192 laser beams using camera sensor images. This paper reviews some of the image processing algorithms that generate the crucial alignment positions.

Centroid stabilization in alignment of FOA corner cube: designing of a matched filter

The current automation of image-based alignment of NIF high energy laser beams is providing the capability of executing multiple target shots per day. An important aspect of performing multiple shots in a day is to reduce additional time spent aligning specific beams due to perturbations in those beam images. One such alignment is beam centration through the second and third harmonic generating crystals in the final optics assembly (FOA), which employs two retro-reflecting corner cubes to represent the beam center. The FOA houses the frequency conversion crystals for third harmonic generation as the beams enters the target chamber. Beam-to-beam variations and systematic beam changes over time in the FOA corner-cube images can lead to a reduction in accuracy as well as increased convergence durations for the template based centroid detector. This work presents a systematic approach of maintaining FOA corner cube centroid templates so that stable position estimation is applied thereby leading to fast convergence of alignment control loops. In the matched filtering approach, a template is designed based on most recent images taken in the last 60 days. The results show that new filter reduces the divergence of the position estimation of FOA images.

Alignment Mask Design and Image Processing for the Advanced Radiographic Capability (ARC) at the National Ignition Facility

The Advance Radiographic Capability (ARC) at the National Ignition Facility (NIF) is a laser system that employs up to four petawatt (PW) lasers to produce a sequence of short pulses that generate X-rays which backlight high-density inertial confinement fusion (ICF) targets. ARC is designed to produce multiple, sequential X-ray images by using up to eight back lighters. The images will be used to examine the compression and ignition of a cryogenic deuterium-tritium target with tens-of-picosecond temporal resolution during the critical phases of an ICF shot. Multi-frame, hard-X-ray radiography of imploding NIF capsules is a capability which is critical to the success of NIFs missions. As in the NIF system, ARC requires an optical alignment mask that can be inserted and removed as needed for precise positioning of the beam. Due to ARC’s split beam design, inserting the nominal NIF main laser alignment mask in ARC produced a partial blockage of the mask pattern. Requirements for a new mask design were needed. In this paper we describe the ARC mask requirements, the resulting mask design pattern, and the image analysis algorithms used to detect and identify the beam and reference centers required for ARC alignment.

The National Ignition Facility: beam area increase

The National Ignition Facility (NIF) is the world’s most energetic laser, having demonstrated in excess of 1.9MJ @351nm with Inertial Confinement Fusion pulse-shapes in July, 2012. First commissioned with 192 operational beamlines in March, 2009, NIF has since transitioned to routine operation for stockpile stewardship, inertial confinement fusion research, and basic high energy density science.

Optical beam classification using deep learning: a comparison with rule- and feature-based classification

Deep-learning methods are gaining popularity because of their state-of-the-art performance in image classification tasks. In this paper, we explore classification of laser-beam images from the National Ignition Facility (NIF) using a novel deeplearning approach. NIF is the world’s largest, most energetic laser. It has nearly 40,000 optics that precisely guide, reflect, amplify, and focus 192 laser beams onto a fusion target. NIF utilizes four petawatt lasers called the Advanced Radiographic Capability (ARC) to produce backlighting X-ray illumination to capture implosion dynamics of NIF experiments with picosecond temporal resolution. In the current operational configuration, four independent short-pulse ARC beams are created and combined in a split-beam configuration in each of two NIF apertures at the entry of the pre-amplifier. The subaperture beams then propagate through the NIF beampath up to the ARC compressor. Each ARC beamlet is separately compressed with a dedicated set of four gratings and recombined as sub-apertures for transport to the parabola vessel, where the beams are focused using parabolic mirrors and pointed to the target. Small angular errors in the compressor gratings can cause the sub-aperture beams to diverge from one another and prevent accurate alignment through the transport section between the compressor and parabolic mirrors. This is an off-normal condition that must be detected and corrected. The goal of the off-normal check is to determine whether the ARC beamlets are sufficiently overlapped into a merged single spot or diverged into two distinct spots. Thus, the objective of the current work is three-fold: developing a simple algorithm to perform off-normal classification, exploring the use of Convolutional Neural Network (CNN) for the same task, and understanding the inter-relationship of the two approaches. The CNN recognition results are compared with other machine-learning approaches, such as Deep Neural Network (DNN) and Support Vector Machine (SVM). The experimental results show around 96% classification accuracy using CNN; the CNN approach also provides comparable recognition results compared to the present feature-based off-normal detection. The feature-based solution was developed to capture the expertise of a human expert in classifying the images. The misclassified results are further studied to explain the differences and discover any discrepancies or inconsistencies in current classification.