LPI 701-100 : LPIC-OT exam 701: DevOps Tools Engineer Exam

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Exam Number : 701-100
Exam Name : LPIC-OT exam 701: DevOps Tools Engineer
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701-100 exam Format | 701-100 Course Contents | 701-100 Course Outline | 701-100 exam Syllabus | 701-100 exam Objectives


Topic 701: Software Engineering
701.1 Modern Software Development (weight: 6)
Weight: 6

Description: Candidates should be able to design software solutions suitable for modern runtime environments. Candidates should understand how services handle data persistence, sessions, status information, transactions, concurrency, security, performance, availability, scaling, load balancing, messaging, monitoring and APIs. Furthermore, candidates should understand the implications of agile and DevOps on software development.

Key Knowledge Areas:

Understand and design service based applications
Understand common API concepts and standards
Understand aspects of data storage, service status and session handling
Design software to be run in containers
Design software to be deployed to cloud services
Awareness of risks in the migration and integration of monolithic legacy software
Understand common application security risks and ways to mitigate them
Understand the concept of agile software development
Understand the concept of DevOps and its implications to software developers and operators
The following is a partial list of the used files, terms and utilities:

REST, JSON
Service Orientated Architectures (SOA)
Microservices
Immutable servers
Loose coupling
Cross site scripting, SQL injections, verbose error reports, API authentication, consistent enforcement of transport encryption
CORS headers and CSRF tokens
ACID properties and CAP theorem

701.2 Standard Components and Platforms for Software (weight: 2)
Weight: 2

Description: Candidates should understand services offered by common cloud platforms. They should be able to include these services in their application architectures and deployment toolchains and understand the required service configurations. OpenStack service components are used as a reference implementation.
Key Knowledge Areas:

Features and concepts of object storage
Features and concepts of relational and NoSQL databases
Features and concepts of message brokers and message queues
Features and concepts of big data services
Features and concepts of application runtimes / PaaS
Features and concepts of content delivery networks
The following is a partial list of the used files, terms and utilities:

OpenStack Swift
OpenStack Trove
OpenStack Zaqar
CloudFoundry
OpenShift


701.3 Source Code Management (weight: 5)
Weight: 5

Description: Candidates should be able to use Git to manage and share source code. This includes creating and contributing to a repository as well as the usage of tags, branches and remote repositories. Furthermore, the candidate should be able to merge files and resolve merging conflicts.

Key Knowledge Areas:

Understand Git concepts and repository structure
Manage files within a Git repository
Manage branches and tags
Work with remote repositories and branches as well as submodules
Merge files and branches
Awareness of SVN and CVS, including concepts of centralized and distributed SCM solutions
The following is a partial list of the used files, terms and utilities:

git
.gitignore


701.4 Continuous Integration and Continuous Delivery (weight: 5)
Weight: 5

Description: Candidates should understand the principles and components of a continuous integration and continuous delivery pipeline. Candidates should be able to implement a CI/CD pipeline using Jenkins, including triggering the CI/CD pipeline, running unit, integration and acceptance tests, packaging software and handling the deployment of tested software artifacts. This objective covers the feature set of Jenkins version 2.0 or later.

Key Knowledge Areas:

Understand the concepts of Continuous Integration and Continuous Delivery
Understand the components of a CI/CD pipeline, including builds, unit, integration and acceptance tests, artifact management, delivery and deployment
Understand deployment best practices
Understand the architecture and features of Jenkins, including Jenkins Plugins, Jenkins API, notifications and distributed builds
Define and run jobs in Jenkins, including parameter handling
Fingerprinting, artifacts and artifact repositories
Understand how Jenkins models continuous delivery pipelines and implement a declarative continuous delivery pipeline in Jenkins
Awareness of possible authentication and authorization models
Understanding of the Pipeline Plugin
Understand the features of important Jenkins modules such as Copy Artifact Plugin, Fingerprint Plugin, Docker Pipeline, Docker Build and Publish plugin, Git Plugin, Credentials Plugin
Awareness of Artifactory and Nexus
The following is a partial list of the used files, terms and utilities:

Step, Node, Stage
Jenkins SDL
Jenkinsfile
Declarative Pipeline
Blue-green and canary deployment
Topic 702: Container Management
702.1 Container Usage (weight: 7)
Weight: 7

Description: Candidates should be able to build, share and operate Docker containers. This includes creating Dockerfiles, using a Docker registry, creating and interacting with containers as well as connecting containers to networks and storage volumes. This objective covers the feature set of Docker version 17.06 or later.

Key Knowledge Areas:

Understand the Docker architecture
Use existing Docker images from a Docker registry
Create Dockerfiles and build images from Dockerfiles
Upload images to a Docker registry
Operate and access Docker containers
Connect container to Docker networks
Use Docker volumes for shared and persistent container storage
The following is a partial list of the used files, terms and utilities:

docker
Dockerfile
.dockerignore


702.2 Container Deployment and Orchestration (weight: 5)
Weight: 5

Description: Candidates should be able to run and manage multiple containers that work together to provide a service. This includes the orchestration of Docker containers using Docker Compose in conjunction with an existing Docker Swarm cluster as well as using an existing Kubernetes cluster. This objective covers the feature sets of Docker Compose version 1.14 or later, Docker Swarm included in Docker 17.06 or later and Kubernetes 1.6 or later.
Key Knowledge Areas:

Understand the application model of Docker Compose
Create and run Docker Compose Files (version 3 or later)
Understand the architecture and functionality of Docker Swarm mode
Run containers in a Docker Swarm, including the definition of services, stacks and the usage of secrets
Understand the architecture and application model Kubernetes
Define and manage a container-based application for Kubernetes, including the definition of Deployments, Services, ReplicaSets and Pods
The following is a partial list of the used files, terms and utilities:

docker-compose
docker
kubectl


702.3 Container Infrastructure (weight: 4)
Weight: 4

Description: Candidates should be able to set up a runtime environment for containers. This includes running containers on a local workstation as well as setting up a dedicated container host. Furthermore, candidates should be aware of other container infrastructures, storage, networking and container specific security aspects. This objective covers the feature set of Docker version 17.06 or later and Docker Machine 0.12 or later.

Key Knowledge Areas:

Use Docker Machine to setup a Docker host
Understand Docker networking concepts, including overlay networks
Create and manage Docker networks
Understand Docker storage concepts
Create and manage Docker volumes
Awareness of Flocker and flannel
Understand the concepts of service discovery
Basic feature knowledge of CoreOS Container Linux, rkt and etcd
Understand security risks of container virtualization and container images and how to mitigate them The following is a partial list of the used files, terms and utilities:

docker-machine
Topic 703: Machine Deployment
703.1 Virtual Machine Deployment (weight: 4)
Weight: 4

Description: Candidates should be able to automate the deployment of a virtual machine with an operating system and a specific set of configuration files and software.

Key Knowledge Areas:

Understand Vagrant architecture and concepts, including storage and networking
Retrieve and use boxes from Atlas
Create and run Vagrantfiles
Access Vagrant virtual machines
Share and synchronize folder between a Vagrant virtual machine and the host system
Understand Vagrant provisioning, including File, Shell, Ansible and Docker
Understand multi-machine setup
The following is a partial list of the used files, terms and utilities:

vagrant
Vagrantfile


703.2 Cloud Deployment (weight: 2)
Weight: 2

Description: Candidates should be able to configure IaaS cloud instances and adjust them to match their available hardware resources, specifically, disk space and volumes. Additinally, candidates should be able to configure instances to allow secure SSH logins and prepare the instances to be ready for a configuration management tool such as Ansible.

Key Knowledge Areas:

Understanding the features and concepts of cloud-init, including user-data and initializing and configuring cloud-init Use cloud-init to create, resize and mount file systems, configure user accounts, including login credentials such as SSH keys and install software packages from the distributions repository Understand the features and implications of IaaS clouds and virtualization for a computing instance, such as snapshotting, pausing, cloning and resource limits.

703.3 System Image Creation (weight: 2)
Weight: 2

Description: Candidates should be able to create images for containers, virtual machines and IaaS cloud instances.

Key Knowledge Areas:

Understand the functionality and features of Packer
Create and maintain template files
Build images from template files using different builders
The following is a partial list of the used files, terms and utilities:

packer
Topic 704: Configuration Management
704.1 Ansible (weight: 8)
Weight: 8

Description: Candidates should be able to use Ansible to ensure a target server is in a specific state regarding its configuration and installed software. This objective covers the feature set of Ansible version 2.2 or later.

Key Knowledge Areas:

Understand the principles of automated system configuration and software installation
Create and maintain inventory files
Understand how Ansible interacts with remote systems
Manage SSH login credentials for Ansible, including using unprivileged login accounts
Create, maintain and run Ansible playbooks, including tasks, handlers, conditionals, loops and registers
Set and use variables
Maintain secrets using Ansible vaults
Write Jinja2 templates, including using common filters, loops and conditionals
Understand and use Ansible roles and install Ansible roles from Ansible Galaxy
Understand and use important Ansible tasks, including file, copy, template, ini_file, lineinfile, patch, replace, user, group, command, shell, service, systemd, cron, apt, debconf, yum, git, and debug
Awareness of dynamic inventory
Awareness of Ansibles features for non-Linux systems
Awareness of Ansible containers
The following is a partial list of the used files, terms and utilities:

ansible.cfg
ansible-playbook
ansible-vault
ansible-galaxy
ansible-doc


704.2 Other Configuration Management Tools (weight: 2)
Weight: 2

Description: Candidates should understand the main features and principles of important configuration management tools other than Ansible.

Key Knowledge Areas:

Basic feature and architecture knowledge of Puppet.
Basic feature and architecture knowledge of Chef.
The following is a partial list of the used files, terms and utilities:

Manifest, Class, Recipe, Cookbook
puppet
chef
chef-solo
chef-client
chef-server-ctl
knife
Topic 705: Service Operations
705.1 IT Operations and Monitoring (weight: 4)
Weight: 4

Description: Candidates should understand how IT infrastructure is involved in delivering a service. This includes knowledge about the major goals of IT operations, understanding functional and nonfunctional properties of an IT services and ways to monitor and measure them using Prometheus. Furthermore candidates should understand major security risks in IT infrastructure. This objective covers the feature set of Prometheus 1.7 or later.

Key Knowledge Areas:

Understand goals of IT operations and service provisioning, including nonfunctional properties such as availability, latency, responsiveness
Understand and identify metrics and indicators to monitor and measure the technical functionality of a service
Understand and identify metrics and indicators to monitor and measure the logical functionality of a service
Understand the architecture of Prometheus, including Exporters, Pushgateway, Alertmanager and Grafana
Monitor containers and microservices using Prometheus
Understand the principles of IT attacks against IT infrastructure
Understand the principles of the most important ways to protect IT infrastructure
Understand core IT infrastructure components and their the role in deployment
The following is a partial list of the used files, terms and utilities:

Prometheus, Node exporter, Pushgateway, Alertmanager, Grafana
Service exploits, brute force attacks, and denial of service attacks
Security updates, packet filtering and application gateways
Virtualization hosts, DNS and load balancers


705.2 Log Management and Analysis (weight: 4)
Weight: 4

Description: Candidates should understand the role of log files in operations and troubleshooting. They should be able to set up centralized logging infrastructure based on Logstash to collect and normalize log data. Furthermore, candidates should understand how Elasticsearch and Kibana help to store and access log data.

Key Knowledge Areas:

Understand how application and system logging works
Understand the architecture and functionality of Logstash, including the lifecycle of a log message and Logstash plugins
Understand the architecture and functionality of Elasticsearch and Kibana in the context of log data management (Elastic Stack)
Configure Logstash to collect, normalize, transform and store log data
Configure syslog and Filebeat to send log data to Logstash
Configure Logstash to send email alerts
Understand application support for log management
The following is a partial list of the used files, terms and utilities:

logstash
input, filter, output
grok filter
Log files, metrics
syslog.conf
/etc/logstash/logstash.yml
/etc/filebeat/filebeat.yml



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LPI 701: PDF Dumps

Laser-brought about thermal grating spectroscopy according to femtosecond laser multi-photon absorption | 701-100 exam Questions and Study Guide

Visualization of the fs-laser triggered grating

determine three shows a single-shot fluorescence photograph of a grating shaped in air through the interference of two 125-fs laser pulses. It changed into recorded with a laser pulse power of two.eight mJ and imaged with the ICCD camera. The fluorescence signal from the grating become accrued from above the intersection volume with a 20 microscope lens (Olympus Plan N). The field of view of the lens become 2.6 × 2.6 mm (in Fig. three the picture became cropped alongside the vertical axis for a higher visualization) and the decision became discovered to be 50.8 lp/mm according to a 1951 USAF decision chart. decreasing the laser pulse energy of both pump laser beams from 2.eight mJ to 500 \(\upmu \)J (measured at the probe extent) didn't affect the length and the width of the grating. The intensity of the grating drops when coming near lower laser energies of the pump laser beams. At laser pulse energies around 290–300 \(\upmu \)J the grating is hardly ever observable. From the recorded photo proven in Fig. 3 three grating fringes are accompanied with a measured fringe spacing of twenty-two.ninety six \(\upmu \)m. altering the polarization of the pump beams before the intersection vicinity did neither affect the intensity nor the form of the grating. This confirms that the followed luminescence from the grating is as a result of fluorescence as opposed to Rayleigh scattering.

figure three

Single-shot fluorescence image of a laser-induced grating in air recorded with a laser pulse energy of 2.8 mJ. The measured grating spacing is 22.ninety six \(\upmu \)m.

Multi-photon fluorescence spectrum of nitrogen

Fluorescence spectra of \(\hbox N_2\) have been measured in a room-temperature flow of nitrogen fuel, which changed into delivered through a 60-mm diameter porous plug. The recorded fluorescence sign is an accumulation of a thousand single-shot indicators. with a purpose to produce a detectable \(\hbox N_2\) fluorescence signal the laser pulse power of the two pump beams became saved at 1.9 mJ. The ensuing emission spectrum is displayed within the excellent part as solid black line in Fig. 4a. For this purpose, a low-decision grating (150 grooves/mm, blaze 300 nm) changed into used in the spectrograph with a view to cowl a big spectral area from 260 to 440 nm and exhibit the vibrational bands of \(\hbox N_2\) and \(\hbox N_2^+\) with unresolved rotational traces. The fluorescence of the 2nd fine gadget, \(\hbox C^three\Pi _u\) \(\rightarrow \) \(\hbox B^3\Pi _g\), from impartial \(\hbox N_2\), and the fluorescence from the vibration bands (0–0 and 0–1) of the first terrible gadget, \(\hbox B^2\Sigma _u^+\) \(\rightarrow \) \(\hbox X^2\Sigma _g^+\), from ionized nitrogen \(\hbox N_2^+\) can also be accompanied in the figure.

The ICCD camera used within the experiment has a low sensitivity for detecting the fluorescence in the 1st tremendous gadget, \(\hbox B^three\Pi _g\) \(\rightarrow \) \(\hbox A^three\Sigma _u^+\), and this band was for this reason no longer investigated within the existing work. The sprint-dotted black line spectrum, displayed within the reduce half, in an depth-flipped edition for readability, is a theoretical spectrum simulated in PGOPHER25 with a rotational temperature \(\hbox T_rot\) = 293 k and a vibrational temperature \(\hbox T_vib\) = 2500 okay. different vibrational transitions from the \(\hbox C^three\Pi _u\) to \(\hbox B^3\Pi _g\) state are marked with black brackets, displaying that the strongest transition is (0–0) at 337 nm. the two vulnerable bands, (0–0) at 391 nm and (0–1) at 428 nm, within the first negative equipment, \(\hbox B^2\Sigma _u^+\) \(\rightarrow \) \(\hbox X^2\Sigma _g^+\), of \(\hbox N_2^+\) are marked with blue brackets. The Gaussian and Lorentzian contributions to the linewidths of the vibrational bands within the simulated spectrum were adjusted to suit the experimental records. All emission bands are well expected via the simulated spectrum. The accompanied emission spectrum confirms that the \(\hbox C^3\Pi _u\) state in \(\hbox N_2\) and the \(\hbox B^2\Sigma _u^+\) state in \(\hbox N_2^+\) have been populated by the 800-nm laser pulses, which requires multi-photon excitation (see Fig. 2).

figure 4

(a) Normalized low-decision \(\hbox N_2\) and \(\hbox N_2^+\) fluorescence spectra with identified vibrational bands of the transitions \(\hbox C^3\Pi _u\) \(\rightarrow \) \(\hbox B^3\Pi _g\) and \(\hbox B^2\Sigma _u^+\) \(\rightarrow \) \(\hbox X^2\Sigma _g^+\). The suitable part (solid black line) suggests the emission spectrum recorded in \(\hbox N_2\) fuel movement at 293 k for a fs-laser pulse energy of 1.9 mJ and the simulated spectrum in PGOPHER is proven in the reduce part (sprint-dotted black line). (b) Normalized high-resolution \(\hbox N_2\) fluorescence spectrum displaying the vibrational bands of the transition \(\hbox C^three\Pi _u\) \(\rightarrow \) \(\hbox B^3\Pi _g\). The excellent half shows the emission spectra recorded in \(\hbox N_2\) fuel movement at 293 ok for 3 different fs-laser pulse energies: 1.three, 1.6 and 1.9 mJ. The decrease part (sprint-dotted black line) indicates the simulated emission spectrum in PGOPHER at 293 k.

determine 4b suggests the normalized emission spectra of the 2nd advantageous system, \(\hbox C^3\Pi _u\) \(\rightarrow \) \(\hbox B^3\Pi _g\), in the spectral location 350–360 nm recorded at three distinctive pulse energies (1.three, 1.6 and 1.9 mJ) using the high-resolution grating (1200 grooves/mm). The dash-dotted black line spectrum, within the reduce part of the determine, which is simulated with PGOPHER for a rotational temperature of 293 okay, i.e. room temperature, consents very well with the experimental spectra. As can also be considered, the rotational “tails” of the (\(\hbox v^\best =0\rightarrow \hbox v^\best \prime \)=1) and (\(\hbox v^\top =1\rightarrow \hbox v^\major \prime \)=2) transitions are observable and it is obvious that the form of those is still the same for all three laser-pulse energies, which means that there is no gigantic power (warmth) deposited to the gasoline. This outcomes suggests that laser heating is insignificant for laser pulse energies as much as 1.9 mJ.

figure 5

(a) excessive-resolution \(\hbox N_2\) and \(\hbox N_2^+\) fluorescence spectra with vibrational bands of the transitions \(\hbox C^three\Pi _u\) \(\rightarrow \) \(\hbox B^3\Pi _g\) and \(\hbox B^2\Sigma _u^+\) \(\rightarrow \) \(\hbox X^2\Sigma _g^+\) recorded at distinctive pulse energies. (b) vigour dependence of the \(\hbox N_2\) fluorescence signal for \(\hbox C^three\Pi _u\) \(\rightarrow \) \(\hbox B^three\Pi _g\) (0–2) transition and \(\hbox N_2^+\) fluorescence signal for \(\hbox B^2\Sigma _u^+\) \(\rightarrow \) \(\hbox X^2\Sigma _g^+\) (0–0) transition.

figure 5a shows emission spectra of the 2nd fine device, \(\hbox C^3\Pi _u\) \(\rightarrow \) \(\hbox B^3\Pi _g\) and the 1st terrible equipment, \(\hbox B^2\Sigma _u^+\) \(\rightarrow \) \(\hbox X^2\Sigma _g^+\) within the spectral area 374–394 nm recorded at diverse laser pulse energies ranging between 1–1.9 mJ. The vulnerable top around 391 nm corresponds to the (0–0) vibrational band in the 1st bad device, \(\hbox B^2\Sigma _u^+\) \(\rightarrow \) \(\hbox X^2\Sigma _g^+\), of \(\hbox N_2^+\). This top is observable for pulse energies 1.three mJ and above, which suggests that ionization should still be insignificant for pulse energies below 1.3 mJ. here is a crucial remark considering the fact that the laser energies used to listing LIGS alerts within the latest analyze are round 500–seven hundred \(\upmu \)J.

Formation of a thermal grating requires power to be kept in excited states of the goal molecule. To examine the feasible paths for such excitation, emission spectra covering two vibrational bands within the 2nd superb gadget of \(\hbox N_2\) and the (0–0) band within the 1st terrible system of \(\hbox N_2^+\) were recorded for four diverse laser pulse energies, as shown in Fig. 5a. determine 5b shows a logarithmic diagram where the intensities of the (0–2) top of \(\hbox N_2\) and the (0–0) peak of \(\hbox N_2^+\) had been plotted against the laser pulse power. The horizontal error bars indicate the heartbeat-to-pulse uncertainty of the laser energy, which changed into estimated to be ± 0.05 mJ. however inclusive of a restrained variety of records elements, the slopes of the signal dependencies imply that the fluorescence emitted within the \(\hbox N_2\) 2nd wonderful equipment follows an 8–9 photon absorption system for pulse energies between 1 and 1.3 mJ. The dependence then becomes plenty weaker, which shows a 3-photon absorption method for pulse energies starting from 1.three to 1.9 mJ, where ionization of \(\hbox N_2\) is greater possible to take place. As may also be considered from Fig. 2, this signal conduct is somewhat smartly supported via the hypothesized 8-photon excitation from \(\hbox X^1\Sigma _g^+\) to \(\hbox a^\prime \leading 1\Sigma _g^+\) state (see the old section on the digital structure of \(\hbox N_2\) and \(\hbox N_2^+\)), from which the \(\hbox B^2\Sigma _u^+\) state can also be reached by a 5-photon technique. this implies an \(\hbox E_pulse^8\) dependence for low pulse energies, which turns progressively to an \(\hbox E_pulse^8-5\) = \(\hbox E_pulse^three\) dependence with increasing pulse energy. The slope of the signal dependence for the \(\hbox N_2^+\) fluorescence in the first terrible device is about 5 ± 0.5, suggesting a 5-photon manner.

evaluation of LIGS signals

A theoretical mannequin according to Eq. (three) and least-squares becoming events was developed to foretell the experimental LIGS sign recorded in \(\hbox N_2\) gas stream at 323 okay. furthermore, the scaling elements (\(\hbox M_i\), \(\hbox M_f\) and \(\hbox M_e\)) have been diverse in an effort to examine the burden of each of them on the mannequin. The leading problem of this strategy is to discover a very good preliminary wager of the parameters (see the constants in table 1). figure 6 indicates measured (circles) and modeled LIGS alerts of \(\hbox N_2\) at 323 ok at seven hundred \(\upmu \)J laser pulse energy. The mannequin predicts the height positions very neatly, that means that the acoustic frequency \(\hbox f_osc\) is neatly chosen. although, a more in-depth seem to be at the fitted curves reveals some discrepancies. allow us to first assume that the signal is barely generated by the “instantaneous” spatially modulated heat trade, i.e. \(\hbox M_f\) and \(\hbox M_e\) in Eq. (three) are set to zero. This thermal manner takes region when \(\tau \) \(\gg \) \(\hbox T_osc\)/2\(\pi \), where \(\tau \) represents time constants \(\tau _tr\) and \(\tau _th\) introduced within the Eq. (three)39. The pink curve in Fig. 6a represents the model with best the instantaneous time period (\(\hbox M_i\)) protected. This model suggests a terrible contract with experimentally measured curve. the primary height does not healthy smartly seeing that it's above the experimental statistics and the primary valley of the fit is beneath the experimentally measured curve (see the magnified inserts). It looks that a sole contribution from the instantaneous warmth switch is insufficient for correct prediction of the sign. for this reason, the finite warmth deposition term (the time period scaling with \(\hbox M_f\)) needs to be blanketed in the model. The green curve in Fig. 6a represents the model where both the instantaneous (\(\hbox M_i\)) and finite time period (\(\hbox M_f\)) are included. The residuals, plotted in the lessen panel of Fig. 6a, reveal that there is a small change between the two modeled signals. it will be mentioned that the preliminary wager for the finite decay constant changed into set to 0.38 ns (see desk 1 for 323 ok) in line with a quenching price regular of ok = 1.15 \(\times \) 10\(^-eleven\) \(\hbox cm^3\) \(\hbox s^-1\) for the 2nd advantageous band \(\hbox C^3\Pi _u\) \(\rightarrow \) \(\hbox B^three\Pi _g\)41. however, there are few feasible collisional rest mechanisms that can make contributions to the finite decay of the observed sign. To anticipate that quenching, and fully within the \(\hbox C^three\Pi _u\) \(\rightarrow \) \(\hbox B^3\Pi _g\) band equipment, is the most effective contribution to \(\tau _f\) is of direction an enormous simplification. besides the fact that children, an investigation of possible molecular power switch tactics that generate warmth for the formation of the thermal grating isn't in the scope of the present work. even so, in the mannequin it became assumed, as a primary approximation, that the power conversion is smartly described by using a single rate, i.e. a finite leisure time \(\tau _f\) = 0.38 ns for 323 okay. This assumption is justified through the fact that the modeled signal shape (green curve in Fig. 6a) predicts the measured signal very neatly.

figure 6

Measured, at seven hundred \(\upmu \)J laser pulse power, and modelled LIGS alerts of \(\hbox N_2\) at 323 k . The modelled signals are in response to Eq. (3). (a) purple curve corresponds to the model containing best the instantaneous term (\(\hbox M_i\)) and the eco-friendly curve is the model where each the instantaneous and finite phrases (\(\hbox M_i\) and \(\hbox M_f\)) are included. (b) eco-friendly curve corresponds to the mannequin containing both the instantaneous and finite terms (\(\hbox M_i\) and \(\hbox M_f\)), while the blue curve shows the mannequin containing the instantaneous, finite and electrostrictive terms (\(\hbox M_i\), \(\hbox M_f\) and \(\hbox M_e\)).

Two modeled signals are shown in Fig. 6b, where the first one incorporates each the instantaneous (\(\hbox M_i\)) and finite (\(\hbox M_f\)) contribution (eco-friendly curve), while the second one nonetheless includes each of those phrases however also an electrostrictive (\(\hbox M_e\)) contribution (blue curve). it is complicated to take a look at any change between two curves, even from the corresponding residuals proven in the decrease panel of Fig. 6b, which skill that the electrostriction can also be negligible. including instantaneous and finite energy redistribution is, in our experiments, already sufficient for an excellent prediction of the sign if the laser energies are stored beneath 500–700 \(\upmu \)J. despite the fact, it is going to be cited, that for better laser energies grating turns into distorted and an electrostrictive (\(\hbox M_e\)) term can have an have an effect on on the modeling, in spite of the fact that a pure thermal signal is analyzed.

determine 7 suggests thermal LIGS (LITGS) signals (circles) recorded in \(\hbox N_2\) fuel movement at five diverse temperatures, ranging from 295 to 753 k at seven-hundred \(\upmu \)J laser pulse power. each sign is a normal price of a hundred single-shot LITGS alerts. The recorded LITGS indicators for temperatures as much as 473 k had effective intensity, and as a consequence, impartial density filters have been utilized in front of the PMT to steer clear of saturation of the detector. although, this may well be averted via cutting back the laser pulse power down to a hundred \(\upmu \)J (4.5 TW/\(\hbox cm^2\)) while a enough sign-to-noise ratio (SNR) changed into maintained.

The mannequin become outfitted to the measured information (circles) with the goal to extract the oscillation frequency (\(\hbox f_osc\)). The fitted curves are represented in green coloration. The dashed black curve in the reduce panel of Fig. 7a suggests the difference between the experimental statistics and the outfitted curve. The residuals between the experimental LITGS signal and the outfitted model have been investigated for all temperature circumstances with a purpose to make sure a official healthy, however is right here best proven for 295 ok. you'll notice that with increasing temperature the variety of oscillation peaks decreases, and the thermal decay increases (\(\tau _th\) decreases). this is in a very good contract with the theoretical calculations as may also be viewed from desk 1.

determine 7

LITGS alerts recorded in \(\hbox N_2\) gasoline circulation at five distinctive temperatures at 700 \(\upmu \)J laser pulse energy. The distinctive panels correspond to alerts recorded at (a) 295 ok, (c) 373 ok, (d) 473 okay, (e) 673 k, and (f) 753 okay. (b) Fourier radically change of the LITGS sign in (a) for 295 k.

From the outfitted model, the oscillation frequency become extracted from each experimental signal. The temperatures have been then decided the usage of Eq. (2). yet another strategy to retrieve the oscillation frequency is to analyze the Fourier transform of the LITGS signal, as shown in Fig. 7b. although, at high temperatures the frequency top within the Fourier transform (inset in Fig. 7f) turns into broader due low number of resolved oscillation peaks and therefore, the uncertainty in making a choice on the oscillation frequency raises.

determine 8 suggests a evaluation between the evaluated temperatures and the temperatures measured with a thermocouple. The vertical error bars indicate the common deviations for a hundred single-shot LITGS alerts for each temperature. In total seven temperatures had been measured and the uncertainty within the derived LIGS temperature for all investigated gas temperatures turned into: \(296.1\pm 1\ \hbox okay\), \(321.7\pm 3\ \mathrmk\), \(382.8\pm 5.eight\ \mathrmokay\), \(493\pm 5.2\ \mathrmk\), \(594\pm 14.7\ \mathrmk\), \(710.9\pm 40.eight\ \mathrmk\), \(763.2\pm 94.2\ \mathrmokay\).

The thermocouple changed into located 2–three mm far from the intersection factor of both laser beams. This became the closest position possible without intruding the dimension. The thermocouple error suggests the edition of the temperature right through the experimental information assortment for each temperature. This error was observed to be 1\(\%\) of the investigated fuel temperatures, which has a negligible have an effect on on the conclusions. It is obvious that the optimal linear healthy between the calculated LITGS temperatures and the measured ones is correct in the dimension uncertainty range. The expanding uncertainty with increasing temperature arises from the uncertainty in precisely deciding upon the oscillation frequency due to the reducing variety of resolved oscillation peaks of the LITGS signals (examine Fig. 7f which has three peaks and Fig. 7a which has 4 peaks). With increasing temperature, the decay of the LITGS signal because of thermal diffusion is sooner. This skill that the grating dissolves sooner in comparison to at room temperature. The uncertainty is also plagued by the sign intensity, when you consider that it impacts what number of oscillation peaks that can be identified. in spite of this, the linear relation between the LIGS derived temperature and the thermocouple temperature suggests the functionality of LIGS as a thermometry technique within the investigated temperature range.

determine eight

Correlation between the LITGS derived temperatures and the temperatures measured through thermocouple. The measurements were carried out in a \(\hbox N_2\) gasoline movement at 700 \(\upmu \)J laser pulse energy.

For laser pulse energies better than seven hundred \(\upmu \)J, the form of the LIGS sign changes profoundly, during which the oscillations are much less general and shifted. The explanation for this transition is that the gasoline gets heated and weakly ionized resulting in a plasma grating42 (see Fig. 9). The thermal grating turns into extraordinarily unstable as a result of the plasma formation. From 1 mJ pulse power (sixty three TW/\(\hbox cm^2\) ) the \(\hbox N_2^+\) (0–0) band shown in Fig. 5a is observable, that means that the ionization threshold is reached. The indicators offered in Fig. 9 had been recorded in \(\hbox N_2\) fuel movement at room temperature for three distinctive pulse energies: 1.5, 2, and a pair of.9 mJ, respectively. As will also be considered in the figure, the general periodic modulation of the sign has disappeared, reflecting that the hydrodynamics in a plasma grating is vastly distinct in comparison to a thermal grating42. The outcomes also display that the signal depth well-nigh continues to be the identical for the investigated laser pulse energies, whereas their sign shapes change irregularly.

determine 9

Recorded single-shot LIGS signals in \(\hbox N_2\) at fs-laser pulse energies 1.5 mJ (green curve), 2 mJ (crimson curve) and a pair of.9 mJ (blue curve) respectively. The inset indicates the Fourier analysis of the signals at these energies.

moreover, through expanding the power the fs-LIGS sign turns into much more unstable due to the enormously nonlinear phenomena panic within the plasma dynamics. moreover, the plasma lifetime is typically on the order of one hundred ps42, however the lifetime of the fs-LIGS signal is on the ns-scale, which means that the hydrodynamic system following the plasma takes a longer time. It is apparent that accurate thermometry in line with the current model requires that the laser pulse energy is kept beneath \(\sim \) 700 \(\upmu \)J. Diagnostic theory at larger pulse energies may potentially be developed, but additional investigations, together with each experimental and theoretical stories, are required with a view to take into account the underlying mechanism of a plasma-grating-pondered fs-LIGS signal.


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