Download Fusion Inventory

[Rikke Greenlee]

<div>So far I installed the fusion inventory on my clients and they do report back to me ( for more then 2 weeks active). </div><div></div><div>Proof of this is that I see my clients reporting back into the agent management + the software repository is being filled up.</div><div></div><div>I also added on the IP range configuration my complete network range.</div><div></div><div></div><div>I've been searching on the remark here above 'the snmp inventory' but how can I get a snmp inventory of my network.</div><div></div><div>Or do you have to this per device?</div><div></div><div>I really do not quit follow on this.</div><div></div><div>Maybe it's so that you first need to do a network discovery - once then found - you need to create a seperate snmp job for each device.</div><div></div><div>I really don't know so if some one could help me out. Any advice is more then welcome</div><div></div><div></div><div></div><div></div><div></div><div>download fusion inventory</div><div></div><div>Download: https://t.co/07BHun8li4 </div><div></div><div></div><div>Gain real-time visibility into inventory across internal and external locations, including goods in transit. Support anytime, anywhere fulfillment with options such as drop-ship, back-to-back, and supplier consigned inventory.</div><div></div><div></div><div>Manage costs and working capital and meet revenue goals by precisely determining the required inventory investments across stocking locations to achieve target service levels, meet customer demand, and deliver customer satisfaction.</div><div></div><div></div><div>Use the power of Oracle Inventory Management with Oracle Fusion Cloud Demand Management to determine optimal inventory levels and replenishment policies for each item location based on demand forecasts and avoid overstocks or stockouts.</div><div></div><div></div><div>I was able to do use fusion-inventory of the PCs on my network and i am amazed by the results.</div><div></div><div>I am afraid anyhow that i am doing something wrong either in fusion-inventory or GLPI site configuration because, because i can see the list of the deployed software on the machines the but "installations" tab remains empty.</div><div></div><div>Therefore i can se the software list and the number of machines where software is installed but i cannot see on what computers to pinpoint the single installations.</div><div></div><div>due to the fact that the plugins itself is doing the job flawlessly i think i made some mistake, maybe i forgot to activate/configure some options in GLPI or the Fusion-inventory plugin? ( ialready used the search function and on google but i was not able to find the solution).</div><div></div><div></div><div>Searching for a way to import fusioninventory output in itop, without having additional extensive software running.</div><div></div><div>There are threads about OCSInventory integration, but that seems to add excessive complexity.</div><div></div><div> talks about integration with fusioninventory-glpi, but that involves PHP file modification (preferably to be avoided), and the link to the tutorial is broken anyway (leads to the main itop page on SF).</div><div></div><div></div><div></div><div></div><div></div><div></div><div>The best strategy is to write/configure your own collector. I understand that you would like to avoid modifying PHP file, this is not required... </div><div></div><div>1. you will just have to write a few PHP lines in new files, usually just 2 or 3 lines are enough, </div><div></div><div>2. You will need to define how to get data from fusioninventory-glpi. How can this tool be queried, SQL, REST, webservices returning CSV ? For each of those 3 methods, a predefined collector exist already with the SDK, so writing your own based on those is mainly a matter of writing configuration files.</div><div></div><div>3. Then you will have to write an XML file specifying the mapping logic between you fusioninventory-glpi data format and the iTop objects logic. The exact syntax depends on the format of your source data (SQL, CSV or JSON).</div><div></div><div>4.then you will need to create DataSynchro in iTop, export the datasynchro definition in XML format</div><div></div><div>5. Install all those files on a machine, having web access to your iTop (and most probably also web access to your fusioninventory-glpi server) and cronify the collector.</div><div></div><div></div><div>Designing the next generation of fusion experimental facilities and prototype power plants requires detailed predictions of the nuclear response of materials. In particular, engineers need to design the plant operation, maintenance and decommissioning with accurate knowledge of the neutron-induced activation of the materials being proposed for regions within the vacuum vessel of magnetic confinement fusion tokamaks (and inside the vessel/chamber of any other fusion concept where neutrons are produced), where the neutron exposure will be highest. For example, computational assessments are vital to understand the radioactive waste arisings that can be expected at the end-of-life (EOL) of future fusion reactors, with results of the severity [1, 2] and volumes [3, 4] of waste being used to drive design refinement [5], consideration of alternative materials [6], and even review of the approach to classification of fusion waste [7]. For example, figure 1(a) illustrates outputs from whole reactor waste assessment predictions for the European demonstration fusion power plant (EU-DEMO), which are only possible with comprehensive nuclear data libraries used by modern and efficient nuclear inventory codes [8].</div><div></div><div></div><div>In some cases, the material and component lifetimes can be impacted by the neutron-induced transmutation (change in chemical composition), even before the radio-activation is taken into account; early removal of components to reduce severe activation and avoid difficulties in handling and decommissioning is a possible solution to the waste challenges faced by fusion, and hence could also be considered as life-limiting. Mechanical, structural and other functional properties of the materials are influenced, sometime detrimentally, by changes in composition. For example, rhenium (Re) concentrations of a few atomic % in tungsten (W), which is a reasonable expectation from predictions of the EOL chemical make-up of W armour tiles in the first wall of the EU-DEMO first-of-a-kind (FOAK) fusion power plant [10], have been shown to significantly reduce the thermal diffusivity, and hence thermal conductivity, of W [11], which could significantly alter the ability of armour tiles made of W from performing the necessary heat removal to avoid melting. There is even emerging evidence that the clustering of transmutation products might impact the performance of the reduced-activation ferritic-martensitic (RAFM) steels being designed for fusion applications [12], despite the fact that transmutation rates are relatively low in these materials (certainly compared to W). On the other hand, it is well-established that the production of gas, hydrogen and helium, via transmutation reactions, can lead to embrittlement and swelling in steels [12, 13], while helium is known to reduce the strength of welds if present in the steel being welded at concentrations as low as ten parts per million [14, 15], and so accurate prediction by nuclear codes of the gas production rates under neutron irradiation is needed to determine the lifetimes of materials.</div><div></div><div></div><div>Reliable nuclear data and high-fidelity in the codes that use them to make transmutation, activation, and transport predictions is critical. Nuclear data, often taking the form of application-specific libraries, are used throughout the fusion reactor lifecycle: from the design phase where transport simulations and inventory (burn-up) calculations are used to predict the shielding efficacy, tritium breeding performance, and radiological hazard of a design; through construction, where those same calculations must be refined and qualified using the as-built configurations to satisfy regulators and gain permissions to operate; to operations, where many of the diagnostics rely on good nuclear data to measure plasma performance; and finally to maintenance and decommissioning, where activation predictions must be accurate to enable the planning of remote handling activities and waste management.</div><div></div><div></div><div>This paper reviews UKAEA efforts to validate nuclear data libraries and test nuclear inventory codes, which predict transmutation, using available experimental data. Recent efforts to perform nuclear data experiments using γ-spectroscopy for fusion relevant elements including Mo are also presented, highlighting the challenges faced when trying to repurpose ageing facilities to obtain high quality irradiations and measurements. We also discuss some rare, successful benchmarking of transmutation predictions from inventory simulations; for W in fission test reactors. Below we begin by discussing an extensive and important fusion-relevant test suite based on experimental decay-heat measurements.</div><div></div><div></div><div>The needs of future nuclear data measurements and benchmark experiments for fusion inventory simulations are discussed throughout the paper because these will help to reduce uncertainty in code predictions and to minimise engineering safety factors (which are costly), which is highlighted by some deficiencies in cross sections that lead to helium production in Fe and C.</div><div></div><div></div><div>This poorer performance is illustrated further by the distribution of reduced-χ2 statistic values in figure 3(b), where ENDF/B-VIII.0 has a smaller proportion of values below two, while both it and JEFF-3.3 are noted to have more high χ2 values above 20 than the either EAF2010 or its modern successor, TENDL-2021. For ENDF/B-VIII.0 and JEFF-3.3 some of the large disagreements (clearly visible as outliers in figure 3(a) have been demonstrated [22] to be due to insufficient coverage of target isotopes and reaction channels. It is noteworthy from figure 3(b) that after more than a decade of development the distribution of χ2 for the latest 2021 version of the automatically generated TENDL library now performs as well as the EAF2010 library that it has largely replaced for fusion applications.</div><div></div><div></div><div>Reliable decay-heat predictions remain an ongoing need for fusion; it is critical that simulations are accurate to avoid either under-engineered cooling, which could lead to damage of materials and components, or over-engineering that is both costly and energy-consuming.</div><div></div><div></div><div>Direct experimental validation of the full transmutation response of a material is challenging because there is no radiological response to measure from stable nuclides. However, there have been two notable successes in the last few years for W, where modern techniques have been employed to accurately identify the concentration of all elements (and nuclides) produced during exposure in fission test reactors. W is the primary material being considered for the plasma-facing armour of magnetic-confinement fusion reactors such as EU-DEMO [5]. However, it is a strongly transmuting element due to high neutron capture cross sections that include giant capture resonances [10, 40]. These changes in composition are expected to be life-limiting for W-based components because they can lead to loss of thermal conductivity (critical in high heat-flux regions) [11] or segregation-induced embrittlement and hardening [41]. Thus, accurate prediction of transmutation in W will be vital for fusion engineering.</div><div></div><div> 31c5a71286</div>