fresh support

Rice-led project receives $1.9M in federal funding to test 5G energy efficiency, more

The project will focus on testing 5G networks for "stability, interoperability, energy efficiency and communication performance." Photo via Getty Images

A team of Rice University engineers has secured a $1.9 million grant from the U.S. Department of Commerce’s National Telecommunications and Information Administration to develop a new way to test 5G networks.

The project will focus on testing 5G networks for software-centric architectures, according to a statement from Rice. The funds come from the NTIA's most recent round of grants, totaling about $80 million, as part of the $1.5 billion Public Wireless Supply Chain Innovation Fund. Other awards went to Virginia Tech, Northeastern University, DISH Wireless, and more.

The project at Rice will be led by Rahman Doost-Mohammady, an assistant research professor of electrical and computer engineering; and Ashutosh Sabharwal, the Ernest Dell Butcher Professor of Engineering and chair of the Department of Electrical and Computer Engineering. Santiago Segarra, assistant professor of electrical and computer engineering and an expert in machine learning for wireless network design, is also a co-principal investigator on this project.

"Current testing methodologies for wireless products have predominantly focused on the communication dimension, evaluating aspects such as load testing and channel emulation,” said Doost-Mohammady said in a statement. “But with the escalating trend toward software-based wireless products, it’s imperative that we take a more holistic approach to testing."

The new framework will be used to "assess the stability, interoperability, energy efficiency and communication performance of software-based machine learning-enabled 5G radio access networks (RANs)," according to Rice, known as ETHOS.

Once created, the team of researchers will use the framework for extensive testing using novel machine learning algorithms for 5G RAN with California-based NVIDIA's Aerial Research Cloud (ARC) platform. The team also plans to partner with other industry contacts in the future, according to Rice.

“The broader impacts of this project are far-reaching, with the potential to revolutionize software-based and machine learning-enabled wireless product testing by making it more comprehensive and responsive to the complexities of real-world network environments,” Sabharwal said in the statement. “By providing the industry with advanced tools to evaluate and ensure the stability, energy efficiency and throughput of their products, our research is poised to contribute to the successful deployment of 5G and beyond wireless networks.”

Late last year, the Houston location of Greentown Labs also landed funds from the Department of Commerce. The climatetech startup incubator was named to of the Economic Development Administration's 10th cohort of its Build to Scale program and will receive $400,000 with a $400,000 local match confirmed.

Houston-based nonprofit accelerator, BioWell, also received funding from the Build to Scale program.


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This article originally ran on InnovationMap.

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A View From HETI

A new generation of technology is making it faster, safer, and more cost-effective to identify CUI. Courtesy photo

Corrosion under insulation (CUI) accounts for roughly 60% of pipeline leaks in the U.S. oil and gas sector. Yet many operators still rely on outdated inspection methods that are slow, risky, and economically unsustainable.

This year, widespread budget cuts and layoffs across the sector are forcing refineries to do more with less. Efficiency is no longer a goal; it’s a mandate. The challenge: how to maintain safety and reliability without overextending resources?

Fortunately, a new generation of technologies is gaining traction in the oil and gas industry, offering operators faster, safer, and more cost-effective ways to identify and mitigate CUI.

Hidden cost of corrosion

Corrosion is a pervasive threat, with CUI posing the greatest risk to refinery operations. Insulation conceals damage until it becomes severe, making detection difficult and ultimately leading to failure. NACE International estimates the annual cost of corrosion in the U.S. at $276 billion.

Compounding the issue is aging infrastructure: roughly half of the nation’s 2.6 million miles of pipeline are over 50 years old. Aging infrastructure increases the urgency and the cost of inspections.

So, the question is: Are we at a breaking point or an inflection point? The answer depends largely on how quickly the industry can move beyond inspection methods that no longer match today's operational or economic realities.

Legacy methods such as insulation stripping, scaffolding, and manual NDT are slow, hazardous, and offer incomplete coverage. With maintenance budgets tightening, these methods are no longer viable.

Why traditional inspection falls short

Without question, what worked 50 years ago no longer works today. Traditional inspection methods are slow, siloed, and dangerously incomplete.

Insulation removal:

  • Disruptive and expensive.
  • Labor-intensive and time-consuming, with a high risk of process upsets and insulation damage.
  • Limited coverage. Often targets a small percentage of piping, leaving large areas unchecked.
  • Health risks: Exposes workers to hazardous materials such as asbestos or fiberglass.

Rope access and scaffolding:

  • Safety hazards. Falls from height remain a leading cause of injury.
  • Restricted time and access. Weather, fatigue, and complex layouts limit coverage and effectiveness.
  • High coordination costs. Multiple contractors, complex scheduling, and oversight, which require continuous monitoring, documentation, and compliance assurance across vendors and protocols drive up costs.

Spot checks:

  • Low detection probability. Random sampling often fails to detect localized corrosion.
  • Data gaps. Paper records and inconsistent methods hinder lifecycle asset planning.
  • Reactive, not proactive: Problems are often discovered late after damage has already occurred.

A smarter way forward

While traditional NDT methods for CUI like Pulsed Eddy Current (PEC) and Real-Time Radiography (RTR) remain valuable, the addition of robotic systems, sensors, and AI are transforming CUI inspection.

Robotic systems, sensors, and AI are reshaping how CUI inspections are conducted, reducing reliance on manual labor and enabling broader, data-rich asset visibility for better planning and decision-making.

ARIX Technologies, for example, introduced pipe-climbing robotic systems capable of full-coverage inspections of insulated pipes without the need for insulation removal. Venus, ARIX’s pipe-climbing robot, delivers full 360° CUI data across both vertical and horizontal pipe circuits — without magnets, scaffolding, or insulation removal. It captures high-resolution visuals and Pulsed Eddy Current (PEC) data simultaneously, allowing operators to review inspection video and analyze corrosion insights in one integrated workflow. This streamlines data collection, speeds up analysis, and keeps personnel out of hazardous zones — making inspections faster, safer, and far more actionable.

These integrated technology platforms are driving measurable gains:

  • Autonomous grid scanning: Delivers structured, repeatable coverage across pipe surfaces for greater inspection consistency.
  • Integrated inspection portal: Combines PEC, RTR, and video into a unified 3D visualization, streamlining analysis across inspection teams.
  • Actionable insights: Enables more confident planning and risk forecasting through digital, shareable data—not siloed or static.

Real-world results

Petromax Refining adopted ARIX’s robotic inspection systems to modernize its CUI inspections, and its results were substantial and measurable:

  • Inspection time dropped from nine months to 39 days.
  • Costs were cut by 63% compared to traditional methods.
  • Scaffolding was minimized 99%, reducing hazardous risks and labor demands.
  • Data accuracy improved, supporting more innovative maintenance planning.

Why the time is now

Energy operators face mounting pressure from all sides: aging infrastructure, constrained budgets, rising safety risks, and growing ESG expectations.

In the U.S., downstream operators are increasingly piloting drone and crawler solutions to automate inspection rounds in refineries, tank farms, and pipelines. Over 92% of oil and gas companies report that they are investing in AI or robotic technologies or have plans to invest soon to modernize operations.

The tools are here. The data is here. Smarter inspection is no longer aspirational — it’s operational. The case has been made. Petromax and others are showing what’s possible. Smarter inspection is no longer a leap but a step forward.

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Tyler Flanagan is director of service & operations at Houston-based ARIX Technologies.


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