German stellarator revamped to run longer, hotter, compete with tokamaks

Tokamaks have dominated the search for fusion energy for decades. Just as ITER, the world's largest and most expensive tokamak, nears completion in southern France, a smaller, twistier testbed will start up in Germany.

If the 16-meter-wide stellarator can match or outperform similar-size tokamaks, fusion experts may rethink their future. Stellarators can keep their superhot gases stable enough to fuse nuclei and produce energy. They can theoretically run forever, but tokamaks must pause to reset their magnet coils.

The €1 billion German machine, Wendelstein 7-X (W7-X), is already getting "tokamak-like performance" in short runs, claims plasma physicist David Gates, preventing particles and heat from escaping the superhot gas. If W7-X can go long, "it will be ahead," he says. "Stellarators excel" Eindhoven University of Technology theorist Josefine Proll says, "Stellarators are back in the game." A few of startup companies, including one that Gates is leaving Princeton Plasma Physics Laboratory, are developing their own stellarators.

W7-X has been running at the Max Planck Institute for Plasma Physics (IPP) in Greifswald, Germany, since 2015, albeit only at low power and for brief runs. W7-X's developers took it down and replaced all inner walls and fittings with water-cooled equivalents, allowing for longer, hotter runs. The team reported at a W7-X board meeting last week that the revised plasma vessel has no leaks. It's expected to restart later this month to show if it can get plasma to fusion-igniting conditions.

Wendelstein 7-X's water-cooled inner surface allows for longer runs.

HOSAN/IPP

Both stellarators and tokamaks create magnetic gas cages hot enough to melt metal. Microwaves or particle beams heat. Extreme temperatures create a plasma, a seething mix of separated nuclei and electrons, and cause the nuclei to fuse, releasing energy. A fusion power plant would use deuterium and tritium, which react quickly. Non-energy-generating research machines like W7-X avoid tritium and use hydrogen or deuterium instead.

Tokamaks and stellarators use electromagnetic coils to create plasma-confining magnetic fields. A greater field near the hole causes plasma to drift to the reactor's wall.

Tokamaks control drift by circulating plasma around a ring. Streaming creates a magnetic field that twists and stabilizes ionized plasma. Stellarators employ magnetic coils to twist, not plasma. Once plasma physicists got powerful enough supercomputers, they could optimize stellarator magnets to improve plasma confinement.

W7-X is the first large, optimized stellarator with 50 6- ton superconducting coils. Its construction began in the mid-1990s and cost roughly twice the €550 million originally budgeted.

The wait hasn't disappointed researchers. W7-X director Thomas Klinger: "The machine operated immediately." "It's a friendly machine." It did everything we asked." Tokamaks are prone to "instabilities" (plasma bulging or wobbling) or strong "disruptions," sometimes associated to halted plasma flow. IPP theorist Sophia Henneberg believes stellarators don't employ plasma current, which "removes an entire branch" of instabilities.

In early stellarators, the magnetic field geometry drove slower particles to follow banana-shaped orbits until they collided with other particles and leaked energy. Gates believes W7-X's ability to suppress this effect implies its optimization works.

W7-X loses heat through different forms of turbulence, which push particles toward the wall. Theorists have only lately mastered simulating turbulence. W7-X's forthcoming campaign will test simulations and turbulence-fighting techniques.

A stellarator can run constantly, unlike a tokamak, which pulses. W7-X has run 100 seconds—long by tokamak standards—at low power. The device's uncooled microwave and particle heating systems only produced 11.5 megawatts. The update doubles heating power. High temperature, high plasma density, and extensive runs will test stellarators' fusion power potential. Klinger wants to heat ions to 50 million degrees Celsius for 100 seconds. That would make W7-X "a world-class machine," he argues. The team will push for 30 minutes. "We'll move step-by-step," he says.

W7-X's success has inspired VCs to finance entrepreneurs creating commercial stellarators. Startups must simplify magnet production.

Princeton Stellarators, created by Gates and colleagues this year, has $3 million to build a prototype reactor without W7-X's twisted magnet coils. Instead, it will use a mosaic of 1000 HTS square coils on the plasma vessel's outside. By adjusting each coil's magnetic field, operators can change the applied field's form. Gates: "It moves coil complexity to the control system." The company intends to construct a reactor that can fuse cheap, abundant deuterium to produce neutrons for radioisotopes. If successful, the company will build a reactor.

Renaissance Fusion, situated in Grenoble, France, raised €16 million and wants to coat plasma vessel segments in HTS. Using a laser, engineers will burn off superconductor tracks to carve magnet coils. They want to build a meter-long test segment in 2 years and a full prototype by 2027.

Type One Energy in Madison, Wisconsin, won DOE money to bend HTS cables for stellarator magnets. The business carved twisting grooves in metal with computer-controlled etching equipment to coil cables. David Anderson of the University of Wisconsin, Madison, claims advanced manufacturing technology enables the stellarator.

Anderson said W7-X's next phase will boost stellarator work. “Half-hour discharges are steady-state,” he says. “This is a big deal.”

The Kardashev scale grades civilizations from Type 1 to Type 3 based on energy harvesting.

How do technologically proficient civilizations emerge across timescales measuring in the tens of thousands or even millions of years? This is a question that worries me as a researcher in the search for “technosignatures” from other civilizations on other worlds. Since it is already established that longer-lived civilizations are the ones we are most likely to detect, knowing something about their prospective evolutionary trajectories could be translated into improved search tactics. But even more than knowing what to seek for, what I really want to know is what happens to a society after so long time. What are they capable of? What do they become?

This was the question Russian SETI pioneer Nikolai Kardashev asked himself back in 1964. His answer was the now-famous “Kardashev Scale.” Kardashev was the first, although not the last, scientist to try and define the processes (or stages) of the evolution of civilizations. Today, I want to launch a series on this question. It is crucial to technosignature studies (of which our NASA team is hard at work), and it is also important for comprehending what might lay ahead for mankind if we manage to get through the bottlenecks we have now.

The Kardashev scale

Kardashev’s question can be expressed another way. What milestones in a civilization’s advancement up the ladder of technical complexity will be universal? The main notion here is that all (or at least most) civilizations will pass through some kind of definable stages as they progress, and some of these steps might be mirrored in how we could identify them. But, while Kardashev’s major focus was identifying signals from exo-civilizations, his scale gave us a clear way to think about their evolution.

The classification scheme Kardashev employed was not based on social systems of ethics because they are something that we can probably never predict about alien cultures. Instead, it was built on energy, which is something near and dear to the heart of everybody trained in physics. Energy use might offer the basis for universal stages of civilisation progression because you cannot do the work of establishing a civilization without consuming energy. So, Kardashev looked at what energy sources were accessible to civilizations as they evolved technologically and used those to build his scale.

From Kardashev’s perspective, there are three primary levels or “types” of advancement in terms of harvesting energy through which a civilization should progress.

Type 1: Civilizations that can capture all the energy resources of their native planet constitute the first stage. This would imply capturing all the light energy that falls on a world from its host star. This makes it reasonable, given solar energy will be the largest source available on most planets where life could form. For example, Earth absorbs hundreds of atomic bombs’ worth of energy from the Sun every second. That is a rather formidable energy source, and a Type 1 race would have all this power at their disposal for civilization construction.

Type 2: These civilizations can extract the whole energy resources of their home star. Nobel Prize-winning scientist Freeman Dyson famously anticipated Kardashev’s thinking on this when he imagined an advanced civilization erecting a large sphere around its star. This “Dyson Sphere” would be a machine the size of the complete solar system for gathering stellar photons and their energy.

Type 3: These super-civilizations could use all the energy produced by all the stars in their home galaxy. A normal galaxy has a few hundred billion stars, so that is a whole lot of energy. One way this may be done is if the civilization covered every star in their galaxy with Dyson spheres, but there could also be more inventive approaches.

Implications of the Kardashev scale

Climbing from Type 1 upward, we travel from the imaginable to the god-like. For example, it is not hard to envisage utilizing lots of big satellites in space to gather solar energy and then beaming that energy down to Earth via microwaves. That would get us to a Type 1 civilization. But creating a Dyson sphere would require chewing up whole planets. How long until we obtain that level of power? How would we have to change to get there? And once we get to Type 3 civilizations, we are virtually thinking about gods with the potential to engineer the entire cosmos.

For me, this is part of the point of the Kardashev scale. Its application for thinking about identifying technosignatures is crucial, but even more strong is its capacity to help us shape our imaginations. The mind might become blank staring across hundreds or thousands of millennia, and so we need tools and guides to focus our attention. That may be the only way to see what life might become — what we might become — once it arises to start out beyond the boundaries of space and time and potential.

This is a summary. Read the full article here.

Orion's red supergiant Betelgeuse erupted. This is astronomers' most magnificent occurrence.

Betelgeuse, a supergiant star in Orion, garnered attention in 2019 for its peculiar appearance. It continued to dim in 2020.

The star was previously thought to explode as a supernova. Studying the event has revealed what happened to Betelgeuse since it happened.

Astronomers saw that the star released a large amount of material, causing it to lose a section of its surface.

They have never seen anything like this and are unsure what caused the star to release so much material.

According to Harvard-Smithsonian Center for Astrophysics astrophysicist Andrea Dupre, astronomers' data reveals an unexplained mystery.

They say it's a new technique to examine star evolution. The James Webb telescope revealed the star's surface features.

Corona flares are stellar mass ejections. These eruptions change the Sun's outer atmosphere.

This could affect power grids and satellite communications if it hits Earth.

Betelgeuse's flare ejected four times more material than the Sun's corona flare.

Astronomers have monitored star rhythms for 50 years. They've seen its dimming and brightening cycle start, stop, and repeat.

Monitoring Betelgeuse's pulse revealed the eruption's power.

Dupre believes the star's convection cells are still amplifying the blast's effects, comparing it to an imbalanced washing machine tub.

The star's outer layer has returned to normal, Hubble data shows. The photosphere slowly rebuilds its springy surface.

Dupre noted the star's unusual behavior. For instance, it’s causing its interior to bounce.

This suggests that the mass ejections that caused the star's surface to lose mass were two separate processes.

Researchers hope to better understand star mass ejection with the James Webb Space Telescope.

Follower count, earnings, and more

I created my 7th TikTok account six months ago. TikTok's great. I've developed accounts for Amazon products, content creators/brand deals education, website flipping, and more.

Introverted or shy people use faceless TikTok accounts.

Maybe they don't want millions of people to see their face online, or they want to remain anonymous so relatives and friends can't locate them.

Going faceless on TikTok can help you grow a following, communicate your message, and make money online.

Here are 6 steps I took to turn my Tik Tok account into a $60,000/year side gig.

From nothing to $60K in 6 months

It's clickbait, but it’s true. Here’s what I did to get here.

Quick context:

I've used social media before. I've spent years as a social creator and brand.

I've built Instagram, TikTok, and YouTube accounts to nearly 100K.

How I did it

First, select a niche.

If you can focus on one genre on TikTok, you'll have a better chance of success, however lifestyle creators do well too.

Niching down is easier, in my opinion.

Examples:

• Travel

• Food

• Kids

• Earning cash

• Finance

You can narrow these niches if you like.

During the pandemic, a travel blogger focused on Texas-only tourism and gained 1 million subscribers.

Couponing might be a finance specialization.

One of my finance TikTok accounts gives credit tips and grants and has 23K followers.

Tons of ways you can get more specific.

Consider how you'll monetize your TikTok account. I saw many enormous TikTok accounts that lose money.

Why?

They can't monetize their niche. Not impossible to commercialize, but tough enough to inhibit action.

First, determine your goal.

In this first step, consider what your end goal is.

Are you trying to promote your digital products or social media management services?

You want brand deals or e-commerce sales.

This will affect your TikTok specialty.

This is the first step to a TikTok side gig.

Step 2: Pick a content style

Next, you want to decide on your content style.

Do you do voiceover and screenshots?

You'll demonstrate a product?

Will you faceless vlog?

Step 3: Look at the competition

Find anonymous accounts and analyze what content works, where they thrive, what their audience wants, etc.

This can help you make better content.

Like the skyscraper method for TikTok.

Step 4: Create a content strategy.

Your content plan is where you sit down and decide:

• How many videos will you produce each day or each week?

• Which links will you highlight in your biography?

• What amount of time can you commit to this project?

You may schedule when to post videos on a calendar. Make videos.

5. Create videos.

No video gear needed.

Using a phone is OK, and I think it's preferable than posting drafts from a computer or phone.

TikTok prefers genuine material.

Use their app, tools, filters, and music to make videos.

And imperfection is preferable. Tik okers like to see videos made in a bedroom, not a film studio.

Make sense?

When making videos, remember this.

I personally use my phone and tablet.

Step 6: Monetize

Lastly, it’s time to monetize How will you make money? You decided this in step 1.

Time to act!

For brand agreements

• Include your email in the bio.

• Share several sites and use a beacons link in your bio.

• Make cold calls to your favorite companies to get them to join you in a TikTok campaign.

For e-commerce

• Include a link to your store's or a product's page in your bio.

For client work

• Include your email in the bio.

• Use a beacons link to showcase your personal website, portfolio, and other resources.

For affiliate marketing

• Include affiliate product links in your bio.

• Join the Amazon Influencer program and provide a link to your storefront in your bio.

$60,000 per year from Tik Tok?

Yes, and some creators make much more.

Tori Dunlap (herfirst100K) makes $100,000/month on TikTok.

My TikTok adventure took 6 months, but by month 2 I was making $1,000/month (or $12K/year).

By year's end, I want this account to earn $100K/year.

Imagine if my 7 TikTok accounts made $100K/year.

7 Tik Tok accounts X $100K/yr = $700,000/year

AI images are like funhouse mirrors.

Google's AI Blog introduced the puppy-slug in the summer of 2015.

Puppy-slug isn't a single image or character. "Puppy-slug" refers to Google's DeepDream's unsettling psychedelia. This tool uses convolutional neural networks to train models to recognize dataset entities. If researchers feed the model millions of dog pictures, the network will learn to recognize a dog.

DeepDream used neural networks to analyze and classify image data as well as generate its own images. DeepDream's early examples were created by training a convolutional network on dog images and asking it to add "dog-ness" to other images. The models analyzed images to find dog-like pixels and modified surrounding pixels to highlight them.

Puppy-slugs and other DeepDream images are ugly. Even when they don't trigger my trypophobia, they give me vertigo when my mind tries to reconcile familiar features and forms in unnatural, physically impossible arrangements. I feel like I've been poisoned by a forbidden mushroom or a noxious toad. I'm a Lovecraft character going mad from extradimensional exposure. They're gross!

Is this really how AIs see the world? This is possibly an even more unsettling topic that DeepDream raises than the blatant abjection of the images.

When these photographs originally circulated online, many friends were startled and scandalized. People imagined a computer's imagination would be literal, accurate, and boring. We didn't expect vivid hallucinations and organic-looking formations.

DeepDream's images didn't really show the machines' imaginations, at least not in the way that scared some people. DeepDream displays data visualizations. DeepDream reveals the "black box" of convolutional network training.

Some of these images look scary because the models don't "know" anything, at least not in the way we do.

These images are the result of advanced algorithms and calculators that compare pixel values. They can spot and reproduce trends from training data, but can't interpret it. If so, they'd know dogs have two eyes and one face per head. If machines can think creatively, they're keeping it quiet.

You could be forgiven for thinking otherwise, given OpenAI's Dall-impressive E's results. From a technological perspective, it's incredible.

Arthur C. Clarke once said, "Any sufficiently advanced technology is indistinguishable from magic." Dall-magic E's requires a lot of math, computer science, processing power, and research. OpenAI did a great job, and we should applaud them.

Dall-E and similar tools match words and phrases to image data to train generative models. Matching text to images requires sorting and defining the images. Untold millions of low-wage data entry workers, content creators optimizing images for SEO, and anyone who has used a Captcha to access a website make these decisions. These people could live and die without receiving credit for their work, even though the project wouldn't exist without them.

This technique produces images that are less like paintings and more like mirrors that reflect our own beliefs and ideals back at us, albeit via a very complex prism. Due to the limitations and biases that these models portray, we must exercise caution when viewing these images.

The issue was succinctly articulated by artist Mimi Onuoha in her piece "On Algorithmic Violence":

As we continue to see the rise of algorithms being used for civic, social, and cultural decision-making, it becomes that much more important that we name the reality that we are seeing. Not because it is exceptional, but because it is ubiquitous. Not because it creates new inequities, but because it has the power to cloak and amplify existing ones. Not because it is on the horizon, but because it is already here.

Scaling your startup requires answering customer demands, not growth tricks.

I cofounded Freedo Rentals in 2019. I reached 50 lakh+ ARR in 6 months before quitting owing to the epidemic.

Freedo aimed to solve 2 customer pain points:

• Users lacked a reliable last-mile transportation option.

• The amount that Auto walas charge for unmetered services

Solution?

Effectively simple.

Build ports at high-demand spots (colleges, residential societies, metros). Electric ride-sharing can meet demand.

We had many problems scaling. I'll explain using the AARRR model.

• Brand unfamiliarity or a novel product offering were the problems with awareness. Nobody knew what Freedo was or what it did.

• Problem with awareness: Content and advertisements did a poor job of communicating the task at hand. The advertisements clashed with the white-collar part because they were too cheesy.

• Retention Issue: We encountered issues, indicating that the product was insufficient. Problems with keyless entry, creating bills, stealing helmets, etc.

• Retention/Revenue Issue: Costly compared to established rivals. Shared cars were 1/3 of our cost.

• Referral Issue: Missing the opportunity to seize the AHA moment. After the ride, nobody remembered us.

Once you know where you're struggling with AARRR, iterative solutions are usually best.

Once you have nailed the AARRR model, most startups use paid channels to scale. This dependence, on paid channels, increases with scale unless you crack your organic/inbound game.

Over-index growth loops. Growth loops increase inflow and customers as you scale.

When considering growth, ask yourself:

• Who is the solution's ICP (Ideal Customer Profile)? (To whom are you selling)

• What are the most important messages I should convey to customers? (This is an A/B test.)

• Which marketing channels ought I prioritize? (Conduct analysis based on the startup's maturity/stage.)

• Choose the important metrics to monitor for your AARRR funnel (not all metrics are equal)

• Identify the Flywheel effect's growth loops (inertia matters)

My biggest mistakes:

• not paying attention to consumer comments or satisfaction. It is the main cause of problems with referrals, retention, and acquisition for startups. Beyond your NPS, you should consider second-order consequences.

• The tasks at hand should be quite clear.

Here's my scaling equation:

Growth = A x B x C

A = Funnel top (Traffic)

B = Product Valuation (Solving a real pain point)

C = Aha! (Emotional response)

Freedo's A, B, and C created a unique offering.

Freedo’s ABC:

A — Working or Studying population in NCR

B — Electric Vehicles provide last-mile mobility as a clean and affordable solution

C — One click booking with a no-noise scooter

Final outcome:

FWe scaled Freedo to Rs. 50 lakh MRR and were growing 60% month on month till the pandemic ceased our growth story.

How we did it?

We tried ambassadors and coupons. WhatsApp was our most successful A/B test.

We grew widespread adoption through college and society WhatsApp groups. We requested users for referrals in community groups.

What worked for us won't work for others. This scale underwent many revisions.

Every firm is different, thus you must know your customers. Needs to determine which channel to prioritize and when.

Users desired a safe, time-bound means to get there.

This (not mine) growth framework helped me a lot. You should follow suit.