(From a Mechanical and Electrical Viewpoint)

Note, Blog’s Title I’ve used – DATA CENTER: PART II.

My last posting was on ‘DATA CENTERS’ – my impression at the time was everyone is familiar somewhat to AI technology. Noup! Not true entirely. This realization came to me after considering some raw comments from some commentators (may be they are Ghost visitors). That doesn’t matter. I’ve decided here to reintroduce Data Center for general public, this time — dug out from scratch.

Beginners Questions are:

What a data center does,

Why do these facilities consume so much energy and

Why mechanical and electrical systems are the most critical part of the Data Center?

Every email sent or received, every video streamed and watched, and every file we worked on is stored in the cloud existing somewhere physically is our ‘Internet.’ To Tech people internet is a network of buildings filled with servers, electrical infrastructure and cooling systems that work continuously to keep data flowing. These buildings are actual Data Centers. In this Blogpost I focused on the systems that keep servers powered, cooled and facilitated to make sure that systems operate with no interruption.

At its simplest level, a data center is a facility designed to store, process and distribute digital information. [Information is a vital parameter as those in the natural world such as mass, charge, or energy. In the most basic sense, information is any kind of description that can be reduced to a simple string of zeros (0) and ones (1) — known as binary digits, or bits. Meaning information can be quantified and manipulated in a digital computer.] Ref. 1

Inside the data centers building there are rows of servers, which are specialized computers responsible for storage, computing and network traffic. Servers operate continuously, they run 24 hrs. a day, every day of the year. This creates an important reality. Servers consume large amounts of electricity, and nearly all of that electrical energy eventually becomes heat. While most people think of data centers as IT facilities, from an engineering perspective they are really energy conversion buildings. Electricity goes in, computing work is performed, and heat comes out. The entire facility exists to manage this process safely and reliably.

Every data center (regardless of size) must enact 3 fundamental tasks:

• Make ascertain continuous power source,
• Determines Continuous cooling,
• Operate nonstop,

Demand for Continuous Operation is the reason why Servers cannot simply shut down in absence of power. Even brief interruptions of power can cause ‘data loss’ or service outages affect thousands of millions of users. As a result, power systems must remain available even when equipment fails or utility power is interrupted. Servers generate heat continuously; therefore, cooling system also must be in action mode incessantly as well. If cooling stops for any reason, Temperature rise quickly forcing equipment to reduce performance or shutdown to protect itself.  Cooling a Data system is not about comfort; it is about equipment survival.

Data Centers are designed for availability. Maintenance and equipment failures and repairs must occur without interrupting operation. This requirement is what drives the heavy use of redundancy throughout both mechanical and electrical systems.

Data centers infrastructures falls into 3 major system groups:

• Electrical systems or electrical infrastructure bring Power to the building and distribute it safely to the server equipment (this includes utility, connections, switch gear, backup and power distribution).
• Mechanical Cooling System (remove heat generated by servers; (include chillers, pumps, air handling units).
• Controls and Monitoring (Electrical and mechanical system units together into one operating unit).

summary: Under constant Electrical loads, cooling demands operate continuously, with no break time at all is the Data Centers that we all dependent on.

Futuristics:

Elon Musk is an AI expert who opined that ‘computing demands from AI will quickly outstrip what we can provide here on Earth.  A “terawatt-scale annual AI compute growth,” will require power to match. That’s a stunning figure.  All the world’s data centers use around 40 gigawatts today.’  Musk predicted that world would need an additional terawatt worth of compute every year.

To encounter the issue, Musk has started considering big server racks in space. powered by 24/7 sunshine. In reality, all the Silicon Valley executives have become obsessed with space-based solar power.                                                                                                                                                 Based on an estimate from SpaceX “space-based solar arrays can generate “more than five-times the energy” of terrestrial ones thanks to 24/7 illumination.”

REFERENCES:

1. Reference 1: Robert M. Hazen and Michael  L. Wong; “Time’s Second Arrow,” W.W. Norton & Company Inc. New York, NY 10110
2. Reference 2. msn news.com Date Published: (5/24/26).

Data Centers are booming in numbers these days. Apple, Google, Alphabet, Meta, Microsoft and many other high-tech companies are the builders and owners of these Data Centers. The expert team of engineers that work there combine technical insight, hands-on support, and global reach to deliver to customers —on time, on spec, and built to last. By connecting cables, data centers connect people, ideas, and innovation – laying the groundwork for a more resilient, sustainable energy future.

In Telecommunication sector: Data Centers support everything from legacy copper systems to next-gen fiber networks. This service spans global rollouts, regional deployments, and local last-mile solutions. Meaning, a telecom provider that’s building from the core or expanding to the edge, data centers work alongside to that specific project to design, deploy, and sustain critical infrastructure with confidence.  

In Energy Sector: Data Centers build connections that keep energy flowing – from underground infrastructure to above-ground innovation. In a world of evolving demands, shifting regulations, and growing sustainability goals, connection means more than just physical infrastructure – it’s about trust, local expertise, and the ability to scale smartly and sustainably. That’s exactly what Data Centers deliver.

Here on Earth, the vast data centers that power artificial intelligence swallow huge amounts of energy. Tech companies are building massive numbers of AI data centers, with more than 4,000 are in operation across USA now. These vast data centers that house thousands of servers and can stretch over a million square feet and have mostly been built within the last decade. Communities living near these Data Centers expressed their concerns about the environmental and financial implications of such facilities in their backyards.

Popped-up DATA CENTERS tentative locations – Across USA

There are two global problems these Data Centers are worsening.  First, burning gas and coal is still heating up the planet. Second, data Centers consume enormous amount of power. Thousands of these AI data centers that popped up all over the country consume enormous amounts of power, which produces even more carbon pollution – and drives up our electric bills.

According to Andrea Marinoni, associate professor with the Earth Observation group at the University of Cambridge, an author of the study found that  these Data Centers release heat through their energy-intensive processes, including computation and powering cooling systems. Marinoni’s research group studied 20 years of remote sensors and found surface temperatures of the area increased by an average of 3.6 degrees Fahrenheit after a data center started operations. In extreme cases, nearby temperatures increase by up to 16.4 degrees Fahrenheit. Case in point – Mexico’s Bajio region, which has become a data center hub.

Andrea M.’s paper which has not been peer-reviewed yet. (See Reference at the bottom)

OpenAI says it’s using over a million GPUs. By count, 1 million GPUs already require enough energy to power a small city. Scaling that to 100 million could demand more than 75 gigawatts of power costing an estimated $3 trillion in hardware alone. On top of that cost is needed for its maintenance, cooling and expansion process. Here is a real-world scenario: Unlike OpenAI and Anthropic, Musk’s AI-based startup xAI has decided to train and power its efforts by choosing to build and run its own infrastructure. xAI’s goal is to build a huge amount of high-end AI hardware. Its Colossus supercomputer alone, is estimated to run on around 200,000 Nvidia H100 GPUs, drawing anywhere between 50 and 150 MW of power from a system supplemented with methane-burning gas turbines. Its use of natural gas-burning turbines to power data centers in and around Memphis, Tennessee. The turbines emit smog-forming pollutants and particulate matter that can lead to increased health risks and an unpleasant odor, among other things.

Pollution from the turbines, which xAI has used in Memphis, Tennessee, for its Colossus 1 and Colossus 2 data centers, has been a major source of local contention for more than a year.

To STOP, we are still at the beginning of this demand. Increased pressure from environmental and civil rights groups over pollution concerns are litigating in court now.

COLOSSUS – World’s Biggest Supercomputer

DESCRIPTION: The world’s biggest supercomputer, Colossus. Built in 122 days—outpacing every estimate—it was the most powerful AI training system yet. Then Musk and his team doubled it in 92 days to 200k GPUs.

Number of GPUs — 200KGPUs     

                      Total Memory Bandwidth -194 Petabytes/s                

                                                     Network Bandwidth per Server – 3.6 Terabits/s     

Storage Capacity – >1Exabyte

The xAI company’s use of dozens of natural gas-burning turbines requires a federal permit. The company’s violation of the Clean Air Act causing harm nearby communities.

REFERENCE: https://www.researchgate.net/publication/403073048_The_data_heat_island_effect_quantifying_the_impact_of_AI_data_centers_in_a_warming_world

END of Message

Dear Readers, followers or commenters of Fbsolarllc.com site. I am happy to let you know that my fourth (4th) book on the Topic of ‘BATTERY’ has been published in all formats lately in my AMAZON Account. Please check that out at your own pace. The publication announcements by Amazon of this book are copied below.

“Solid State Batteries (SSBs) For EVs” is now live and available for purchase in the Kindle Store!

Solid State Batteries (SSBs) For EVs: A Comprehensive Look

https://a.co/d/0aEjbhxe

Hardcover, “Solid State Batteries (SSBs) For EVs,” is available to buy on Amazon.

Paperback, “Solid State Batteries (SSBs) For EVs,” is published on Amazon & available to buy as well.

Hardcover Image has shown here.

For now, the TITLES of my Four (4) BATTERY books I mentioned below to facilitate your collection drive. If you haven’t collected those books yet, go and buy the books at your most permissible time. Most importantly – enjoy the most UpToDate technical knowledge on Electric Charge STORAGE concern.

End Note: I hope you will collect all the books in the above list, to know so much about all aspects of Battery and Battery Technology. Once you have all the copies- make sure that quality time is available to you to read the books in a meaningful way. At that point you may also have obtain ‘mastery’ on Electric Charge Storage topic. Go, buy for yourself and persuade your friend to buy another set. Finally, let me know your active participation in this Battery Book Drive Campaign.

PART III

Deep Blue, Deep-Mind, DeepSeek, Deep Learning, Deep Research, Deep Think. Google Brain

So, what’s the definition of the WORD ‘Deep’ in AI vocabulary set?

The word “deep” stands for – extending far down from the Top or Surfaces.

In AI, ‘deep’ introduces multiple layers of neural networks that help the system understand and interpret data. The technique allows computers to recognize patterns and manage complex tasks, such as translating languages and driving cars autonomously.

Deep Blue is IBM’s computer (beaten Gary Kasparov in chesses in 1977)

Deep Mind founder is Demis Hassabis, a London based company. Founded for AI → AGI, DeepMind’s research aimed to develop more helpful AI agents by translating advanced AI capabilities into real-world actions through a language interface.

DeepSeek, a Chinese AI chatbot that can outperform some of its best competitors, such as OpenAI’s ChatGPT o1. DeepSeek said it trained its V3 chatbot in just two months with a little more than 2,000 Nvidia H800 GPUs, chips (designed to comply with export restrictions the US placed on China in 2022).

Deep Learning is a subset of machine learning, which in turn is a subset of artificial intelligence (AI).  It is called deep because it makes use of deep neural networks to process data and make decisions. The word ‘deep’ in ‘deep learning refers to the number of layers through which the data is transformed. Deep-learning pioneer is Geoffrey Hinton. [Deep-Learning models performed much better when they are bigger.

Deep Research is a Model in OpenAI that can uncover and discover new knowledge for itself. And the first step here is a model that can go and synthesize.

Gemini 2.5 Deep Think (A Reasoning model) – Google detailed how its Gemini 2.5 models, including 2.5 Pro, are going to advance in the near future. To begin, Gemini is going to receive Google’s 2.5 Flash model. The company touted this AI as its “most powerful” version, but its latest updates improve benchmarks for reasoning and multimodality. Google says 2.5 Flash is now better (more efficient) at code and long context.

Google Brain worked directly on improving Google products.  Andrew Ng, a Stanford Univ. Professor wanted to build more advanced AI system from inside Google. For AGI to build “increasingly accurate approximations to small parts of mammalian brain,” is the requirement. Ng discovered that neural models could do more things if they had more nodes, layers and data to train on. After years of research Google Brain found that it failed to reach its objective.

OpenAI competitors are like Google DeepMind, Meta, xAI, and Anthropic. At this point ChatGPT from OpenAI is uncontested, potentially making it difficult for its rivals like Copilot and Gemini to play catch-up.

END of PART III

PART II

OpenAI has introduced breakthrough products such as GPT-3, DALL-E, and ChatGPT, each pushing the boundaries of what AI can achieve. These achievements demonstrated OpenAI’s technical prowess but have also sparked global conversations about the future of human-computer interaction. They create works of their own by drawing on patterns they’ve identified in vast troves of existing, human-created content. ChatGPT, along with text-to-image tools such as DALL-E 2 and Stable Diffusion, is part of a new wave of software called generative AI.

Beginning of (AI) – The seeds for the real AI revolution were germinated in the fertile soil of the University of Toronto, where Geoffrey Hinton and his colleagues developed the techniques of deep learning, which would later become the basis of generative AI. Generative AI depicts the technique & technology to create new information. The generative AI models we use today are relatively theory-free—their emergent understanding of the world comes from neural-net machine learning techniques and mountains of data (see Part I). This technique has indeed made AI more human like.

GPT stands for Generative Pre-training Transformer. Not an exact AI but an AI model built by Open AI. ChatGPT is an app. GPT is the brain behind that app. With research and development GPT has several versions available – choosing o3 for reasoning, GPT-4.1 for coding, and GPT-4o for regular chats, GPT-5 (soon available) will pick the tools it needs to answer your prompt on its own.

The T in ChatGPT stands for” transformer.” Transformers are a system that allows machines to generate humanlike text. The transformer has become critical to the new wave of generative AI that can produce realistic text, images, videos, DNA sequences, and many other kinds of Data. The transformer’s invention (by Google) in 2017 was about as impactful to the field of AI as the advent of smartphones was to customers.  Transformers also broadened the scope of what AI engineers could do. Handle far more data, process human language much more quickly.

Transformers broke chatbots out of any binding rule. Now Chatbots can deal with nuance and slang. They can refer back to the thing that has been said a few sentences earlier, can handle almost any random query and give a personalized answer. To many AI researchers, that means a step toward AGI, opening a debate about whether computers were starting to “understand language in the same way humans did or, if they were still just processing it through math-based predictions.

The GPU, or graphics processing unit – these are powerful semiconductors (chips) made by Nvidia, runs most of the servers training AI models today. This is a specialized chip originally designed to render images and video. But in the world of AI, GPUs have become the powerhouse behind large language models (LLMs) like ChatGPT.

Unlike CPUs (central processing units), which handle one task at a time very efficiently, GPUs are built to perform thousands of simple calculations simultaneously. That parallel processing ability makes them perfect for training and running AI models, which rely on massive amounts of data and mathematical operations.

Google engineers developed LaMDA (Language Model for Dialogue Applications). This was the company’s Large Language Model project that was based on the Transformer. Large Language Models (LLMs) themselves are very interesting. Their responses were mostly scripted, and they’d often make wacky mistakes. To make computers better at talking and listening

Conclusion: Both Data & Computing power is needed for AI Research, Training & Development.

 END of Part II

PART I

Records show that- AI has famously beaten humans in other high-profile competitions. In 1997, IBM’s Deep Blue AI machine beat chess grandmaster Gary Kasparov, and in 2016, Google DeepMind’s AlphaGo beat Go world champion Lee Sedol.

But not this time around.  Human effort regained its top position. Filling the air with a new spirit. This time, AI failed killing the human coding star.

On Wednesday (7/16/25), a programmer from Poland, Przemysław Dębiak, beat an OpenAI tool at the AtCoder World Tour Finals 2025 Heuristic Contest in Tokyo, which is sponsored by the company. Dębiak, who competed under the name “Psyho,”

In February 2025, Sam Altman said that by the end of this year, OpenAI could surpass humans in coding competitions. AI is already writing big chunks of code at Microsoft, Google, and Meta, the companies’ CEOs have all said in recent months.

Machine learning is a type of AI that allows computer programs to learn when exposed to new data without being programmed. The official idea & definition of AI was first coined by Jay McCarthy in 1955 at the Dartmouth conference. McCarthy proposal is under quotehere.

“Every aspect of learning or any other feature of intelligence can be made to stimulate it. An attempt will be made to find out how to make machines, use language from abstractions and concepts, solve kinds of problems now reserved for humans and inspire thoughts.” In essence, AI is a machine with the ability to solve problems that are usually done by us humans with our moral intelligence. Therefore, AI is about making intelligence universally accessible and making everyone massively more productive.

OpenAI is an AI research and Development Company. This is an organization that creates and promotes friendly AI,. AI code can be kept private, OpenAI approach is where the code is made publicly available, often as open-source software. This enables others to inspect the code to understand how the AI was trained and operates and potentially modify and improve upon it. OpenAI utilizes advanced machine learning techniques, such as deep neural network to …

A neural network is a type of software that gets built by being trained over and over with lots of data. Once it’s been trained, it can recognize faces, predict chess moves, or recommend a Netflix movie for a customer. Also, known as a ‘model’ a neural network is often made up of many different layers and nodes that process information in a vaguely similar way to our brain’s neurons. The more the model is trained, the better those nodes get at predicting or recognizing things.  

The OpenAI organization Musk founded with Sam Altman. In 2015, Musk and Altman co-founded OpenAI together but now run competing AI companies. Founded as a nonprofit OpenAI later turned to seek profits. it took OpenAI seven years (from its founding in 2015) to release the first version of its chatbot, ChatGPT. So, What ChatGPT can do? ChatGPT, is able to converse about the concept of…, can talk about religion and else.  Musk parted ways with OpenAI in 2018. In 2023, Musk founded a rival AI start-up, xAI and launched a chatbot called Grok to compete with ChatGPT. xAI is the company behind Grok, which runs a chatbot on Musk’s X platform.  Musk’s xAI is running on 100,000 of Nvidia’s more advanced H100s chips at a computing cluster in Tennessee. These chips usually retail for $30,000 each.

END of – PART 1

From Google:

Neural Networks: An Overview

Neural networks, also known as artificial neural networks (ANNs), are computational models inspired by the structure and functioning of the human brain. They consist of interconnected nodes (neurons) organized into layers: an input layer, one or more hidden layers, and an output layer. These networks are a cornerstone of machine learning and deep learning, enabling tasks like pattern recognition, decision-making, and data classification.

Each neuron processes input data by applying weights, biases, and an activation function. If the output exceeds a threshold, the neuron activates and passes data to the next layer. This process is known as forward propagation. Neural networks learn by adjusting weights and biases through backpropagation, which minimizes errors using optimization techniques like gradient descent.

On May 20, 2025 (Tuesday), a Falcon 9 rocket lifted off carrying 23 of the company’s Starlink broadband satellites to low Earth orbit (LEO). They’re scheduled to be deployed 65 minutes after launch.  By design, the Starlink satellites get released afterwards to begin individually maneuvering into more specific orbits to join SpaceX’s growing megaconstellation. Refreshing memory:  Starlinks are space-based broadband satellites. SpaceX’s growing megaconstellation   is building on Starlinks, designed to provide high-speed internet access across the globe.

At the moment (and counting continues…) Starlink is the largest satellite megaconstellation ever constructed, consisting of about 7,500 operational satellites. And that number is growing all the time, with every batch of satellites (containing~ 21/ 23/ 27/29…) that reached @ LEO via Falcon 9 carrier. In every batch of satellites, — there included a certain number of (X ~ 7-13) satellites with direct-to-cell capability — to lower Earth orbit.

Per plan, SpaceX’s Starlink network will consists eventually of more than 12,000  satellites by adding batches of satellites counting 2/3 times a month.  As a whole, megaconsttellation  operate in a grid that blankets nearly the planet entirely, saving North & South poles only. Starlink offers users a high-speed internet connection from anywhere (other than the poles), customers are able to point their Starlink receiver toward the sky. Satellites orbit at 342 miles (540km) above the Earth. The Space Station (ISS) is about 250 miles (400km) above the Earth’s surface.

Starlink Megaconstallation position, ISS Orbital Path and Our sweet Earth (not on Scale)

7,500 is a huge number!  So, how the satellite network look like now. Flying with ISS, NASA scientists have recorded a video recently. Keep in mind, the ISS position is 250 miles above you and me. Staying approximately 100 miles below the satellites position NASA scientists recorded a video of the network and named it  “Starlink Satellites Swarm Like ‘Cosmic Fireflies’”

Watch the Time-Lapse from Space video here:

https://www.msn.com/en-us/news/technology/spacex-starlink-satellites-swarm-like-cosmic-fireflies-in-time-lapse-from-space/vi-AA1EMuZ3?ocid=msedgdhp&pc=DCTS&cvid=bc393cb58cf84251bc69ec16305c9a5d

NASA astronaut Don Pettit captured SpaceX Starlink satellites swarming in the distance in an amazing time-lapse captured from the International Space Station. Credit: Space.com | footage courtesy: NASA/Don Pettit | edited by Steve Spaleto 

Do you know SPACEX’s Drone ships name? Here are three SpaceX drone ships name: “Just Read the Instructions,” “A Shortfall of Gravitas, ” and “Of Course I Still Love You,” is in the Pacific Ocean.

Euclid mission is designed to explore the composition and evolution of the dark Universe. Astrophysicist Carole Mundell, the European Space Agency’s director of science, called Euclid a “dark detective.” Euclid is the name of a space telescope (a European satellite) launched in 2023, promising more precise gravitational lensing (GW) data to acquire.

Over its six-year mission, Euclid will observe 1.5 billion galaxies, offering unprecedented insights into the Universe’s history and allowing scientists to test Einstein’s equations more rigorously. Using the data from Euclid, the scientific community is poised to refine its understanding of gravity and cosmic expansion. These efforts may ultimately reshape our perception of the Universe, bringing us closer to solving one of science’s greatest enigmas of dark stuffs of DE & DM (Dark Energy, Dark Matter).

How Euclid Works?

Light traveling toward Earth from faraway galaxies is bent and distorted by normal matter and dark matter in the foreground in an effect called gravitational lensing. It is one of the tools used by Euclid to study how dark matter is distributed. Researchers have been sifting through the Euclid data to identify strong gravitational lenses and so far, have identified about 500 such candidates.

Euclid measures the various shapes and the distribution of galaxies with its high-resolution imaging visible instrument, or VIS, while its near-infrared instrument, or NISP, determines galaxy distances and masses. Euclid eventually is expected to obtain images of more than 1.5 billion galaxies.

Newly released Euclid images hint at the large-scale organization of galaxies in what is known as the cosmic web, essentially the backbone of the universe. In this web, there are concentrations of galaxies with voids of space in between, as if the galaxies were sitting on vast empty bubbles. The cosmic web’s structure offers clues about dark matter and dark energy, according to the researchers.

“Euclid is the best instrument ever built for finding these, because it can take an exceptionally sharp image of wide patches of sky â€Â,“ said University of Toronto astrophysicist Mike Walmsley, one of the Euclid scientists.

Dark matter has baffled physicists for nearly a century, quietly shaping our universe’s unseen framework. Invisible yet influential, it provides the gravity necessary to hold galaxies together, driving their motion and structure. About 85% of all matters in the universe stay hidden, undetectable even with today’s best technology. Frustrating to detect DM, some scientists just declared that ‘there is no existence of Dark Matter at all,’ which I reported in my earlier posting (DE, DM. M & AM – please check).   In the 1930s, researchers first noticed abnormalities in how galaxies moved, suggesting something invisible exerted gravitational pull. Decades later, studies of the cosmic microwave background—the lingering radiation from the universe’s birth—confirmed dark matter’s importance in shaping cosmic evolution.

CONCLUSION: The search for dark matter is a central pillar of modern physics, driving advancements in technology and theory. Direct detection experiments, such as those conducted deep underground, continue to push the boundaries of sensitivity, aiming to capture fleeting interactions between dark matter particles and ordinary matter.

REFERENCE:

https://www.msn.com/en-us/news/technology/european-space-agency-releases-new-data-that-could-help-us-understand-the-nature-of-the-universe/ar-AA1Bg1ka?ocid=socialshare&pc=DCTS&cvid=75fb8b88e3584143afc3010cdcbadf30&ei=38

EU informed about EUCLID’s Detection

The Ivanpah plant is set to begin closing in 2026, with units withdrawn to prepare the land to potentially be repurposed for a different kind of solar power.

In 2014, the plant started commercial operations as the world’s largest concentrated solar thermal plant (CSP), spread across around 5 square miles of federal desert in Ca, USA.  The project was a symbol of the exciting progress across the industry.

Plant’s operating formula — Hundreds of thousands of computer-controlled mirrors called “heliostats” track the sun and concentrate its rays onto three towers, each around 450 feet tall and topped with water-filled boilers. The sun’s hyper-concentrated energy turns this water into steam, which drives a turbine to create electricity. A full description of the plant operation/description is available in the book [“Solar Panel Improvement: 1950 – 2016 For Solar, By Solar, To Solar“] available in Amazon.

One of the key selling points of this solar technology is the ability to store heat, allowing the production of electricity at night or when the sun isn’t shining without needing batteries.

Jenny Chase, a solar analyst at Bloomberg NEF analyzed the failure of this project identifying the primary reasons behind.   These kinds of plants “are just technically really difficult to operate,” Chase told CNN recently. All the tricky, mechanical parts of a fossil fuel plant combined here to act — to run a turbine and to maintain many moving parts — for a distributed energy source these are the challenges.  Mirrors tracking the sun entirely is a tech dependent step therefore some uncertainties follow the step always. Chase pointed out, “It’s really hard to get those all lined up perfectly and keep them lined up at all times.”

And then the biggest problem noted for Ivanpah is the type of photovoltaic solar used — today’s technology use to manufacture solar panels — became so sophisticated yet super cheap.

At the time Ivanpah was built (2007-2009), it was beyond conceivable that photovoltaics is going to be so advanced and efficient, along with the smart batteries availability all the time to accomplish the aforementioned task in a safe and efficient manner. Chase exemplified by saying anyone can buy a solar module for the price of a fence panel.

The plant’s co-owner NRG (North American Energy & Home Services Company) company said prices were competitive when the power agreements were signed in 2009. But over time, advancements in other types of solar technology “led to more efficient, cost effective and flexible options for producing reliable clean energy.”

For some, Ivanpah now stands as a huge, shiny monument to wasted tax dollars and environmental damage. For others, failures like this are a natural part of the race to find the winning solutions for the clean energy transition. Me, as one Solar Energy advocate, I support fully the last reasoning, because – it is the science & innovation that we are talking about here. Unless we try it, we will never know. As the NASA Director once said: “They Try, They Fail, They Fix, They Fly.”

Ivanpah – The CSP/Thermal Project @ Mojave Desert in CA, USA

REFERENCE:

https://www.msn.com/en-us/news/us/this-alien-like-field-of-mirrors-in-the-desert-was-once-the-future-of-solar-energy-it-s-closing-after-just-11-years/ar-AA1yXSuB?ocid=socialshare&pc=DCTS&cvid=948ad8d88ead447488aac5b25ead1d19&ei=18

IVANPAH – SOLAR PLANT IS CLOSING

Tesla entered validation testing of its very first Dry Cathode 4680 cells in July of 24 for a prototype Cybertruck.  Since then, a ramp-up in the battery production factory means that Tesla is satisfied with the production of these cells. Afterwards, Tesla produced enough cells to build nearly 60,000 Cybertrucks. Ramping-up is the key to the cost reduction goal across the lineup.

The 4680 batteries not only incorporate bold design innovations but also optimizes the manufacturing process with unique approaches such as dry electrode coating, integrated tab and ear cutting, laser welding, and the integration of Cell-to-Chassis (CTC) with die-casting. These advancements significantly enhance the battery’s performance, efficiency, and controllability of production costs. Tesla realized early on that whoever possesses the most reliable battery technology will control the future of new energy vehicles

Tesla’s 4680 Dry Electrode technology   

Tesla incorporated Dry Electrode technology in the design of its 4680 batteries.

 Manufacturing process — specifically is the ‘Dry Battery Electrode’ process [Powder to Film].   Original technology was that Tesla acquired in a buyout of a company called Maxwell in 2019.  Successful iteration implementation of the technology reduces size & cost of battery production drastically.

                            This Technology can be applied to both the anode and cathode simultaneously.

Dry Electrode Process                                                                                                                                

The inside of a cylindrical 4680 battery cell is essentially just long metal ribbons that are raked up tight and placed inside a metal can. One ribbon is for the positive side and one for the negative side. Tesla figured out early on how to make a half of the battery in a Dry Process. The anode or negative side of the battery is easier and cheaper side of battery to manufacture. It is mostly just a sheet of copper foil with graphite on top and a bit of silicon mixed in.                                                            

Much more complex side is Cathode, the +ve side is more resistant to dry manufacturing.                                                                                                                    

Al, Ni, Mn, and Co resides in the battery. Battery cathodes have to go through a process called ‘Calendaring.’ It is basically just compressing the powdered material down to a particular thickness (~55micron). The cathode goes through rollers where it presses out flat like a pasta dough. Dry cathode material is too hard. In order to avoid any damage to the roller, Tesla uses hardened steel drums with chrome plated making a perfectly smooth surface on the exterior. Tesla also upgraded more extensively the production equipment and a change in the manufacturing procedure.  With all these changes Tesla finally overcame a cost issue after 5 years of tiresome experimentation, the Dry Cathode problem has finally been solved.

Anode + Si  → More Li ions to be stored in this side, accepts more (+) charges

Active Cathode particles are mixed with polytetrafluoroethylene (PTFE), which fibers the material. The powder is then directly rolled into a thin film and pressed onto aluminum or copper foil to create the electrode sheets. Dry electrode technology allows for the addition of more material to the cathode, as the density of the powder is higher than that of the liquid. As a result, the thickness of the electrode material increases from 55 microns to 60 microns, which enhances the activity of the electrode and boosts energy density by about 5%, further extending the battery’s cycle life. Additionally, the removal of solvents lowers production costs.

Tesla’s 4680 batteries

Jelly Roll -Inside 4680 Cell

Inside the cell there are 3 main layers, the anode, cathode and separator. They are long thin sheets that get coiled up tightly and they are called Jelly roll. But sometimes that roll doesn’t line up perfectly. If the roll does not fit the case properly then the cell has to be rejected. Now, for the issues associated  with the unique design of the 4680 cells along with the dry battery electrode process (Powder → Film;  originally introduced by Maxwell Industries, Tesla perfected it based on its choice).

The tablet’s design is also an industry first and the much longer jelly roll for the much wider cylindrical cell means they would start to hit some alignment issues. These all make perfect sense, not a matter of concern at all. Already a FIX for most of these issues in

Next Generation 4680.

By adding more intrigue to the situation, — a follow-up report is presented here. Tesla has begun implementing an entirely new machine to produce the 4680 cells. An upgrade in efficiency that combines 3 separate machines into one single machine.

3 – in 1 Machine

Machine 1 – Notching       Machine 2 -Winding        Machine 3 – Welding

The three machines are responsible for #1: laser cutting the flags into the edge of the electrode foils and then folding them over using puffs of compressed air to create the tablets connections. #2: Rolling those electrode sheets with the separator layer into tight jelly roll. #3. A welding machine that attaches the current collector plates to the tablet’s terminal end. This current collector is placed in between the jelly roll and the end cap of the cell casing.

Gen 2 cells are distinguishable by a unique design on the bottom of the cell with spiral indentation pattern. [See Reference 3] This is the most recent change brought to the cell design, but no one really knew what it was. The new part is now serving double duty as both the current collector and the end cap. The three-pointed indentation is laser welded directly to the tablet’s electrode terminal and then the liquid electrolyte is added through the hole in the center before the case closed. By removing separate current collector Tesla is actually freeing up more space inside the cell casing. That means they can actually make the jelly roll taller to fill that excess space. Excess space, which means more active electrode material means a scope for more ED in the cell.

TESLA is now building Cybertruck with the Dry Cathode process. Battery cathodes in this truck were made on mass production equipment. Machines (Giga press) made these batteries ready and remained standby to roll over again (100 million or so)- for volume production of Cybertrucks. Analysis below:

Ni, Co, Mn (NCM), lithium-ion cathode chemistry

1344 individual battery cells in one Tesla Cybertruck battery with a combined total energy of 123kWhs.  (individual cell has 91.5Wh of capacity. [Total wt of battery ~1600ibs. Heavy]

Battery is a structural element of the vehicle.

An extended range of about 38%  = 47kWh (Total 170kWh —-470miles)

~10% more energy-dense than the initial ones

Since there is no liquid in these batteries so why the batteries aren’t considered as Solid-State battery? With NCM chemistry and higher ED, the batteries are demonstrating higher range and more efficient. CEO of Tesla motor spoken highly about Dry Cathode 4680 cell. It is now time to evaluate Dry Cathode 4680 cells to consider as ‘on hand SSbs.’ Dreaming for SSBs which are still distant products —all manufacturers, consumers and reviewers everyone in EV line should consider this DDB swap. This is my opinion only.

REFERENCE:

https://www.msn.com/en-us/autos/news/the-real-reason-tesla-developed-the-4680-battery/vi-AA1xMmB1?ocid=msedgdhp&pc=DCTS&cvid=d8b7843058904c969338f4176691efdd&ei=22

https://www.msn.com/en-us/money/other/the-truth-about-solid-state-batteries-how-close-are-they/vi-AA1xVCzd?ocid=msedgdhp&pc=DCTS&cvid=431b170df3fe4034fba4fc6295967147&ei=21

About Battery

https://www.msn.com/en-us/autos/news/the-gen-2-tesla-4680-battery-cell-is-here/vi-AA1xnpzT?ocid=msedgdhp&pc=DCTS&cvid=a8fecc84f88341bdc388cf8d5b41adbf&ei=44

About 4680 – latest update