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HBCU University Day | Abstract Submissions

This article was updated on October 14, 2020 to reflect the new event date, February 11, 2021. The coronavirus pandemic introduced unforeseen logistical hurdles in hosting the event during the school semester, so we’ve postponed this event to accommodate university researchers.

[vcex_button url=”” title=”Agenda” style=”Flat” align=”right” margin=”20px” color=”blue” size=”medium” target=”blank” rel=”none”]Download Agenda[/vcex_button]The U.S. Air Force Research Lab (AFRL) wants to build relationships with scientists and engineers teaching or studying at Historically Black Colleges and Universities. The Doolittle Institute, as part of our relationship with AFRL, is looking for potential collaborators who are researching or have already completed research in machine learning, additive manufacturing, and advanced manufacturing to present their research in HBCU University Day on February 11, 2021.

Some of the potential opportunities for selected presenters include: 

  • Seedling funding and Sponsored research 
  • Fellowships/Summer scientist programs at the AFRLMunitions Directorate 
  • Cooperative Research and Development Agreements (CRADA) 
  • Educational Partnership Agreements (EPA) 

[vcex_button url=”″ title=”Submit” style=”flat” align=”right” margin=”20px” color=”red” size=”medium” target=”blank” rel=”none”]Submit Abstract[/vcex_button]

Submissions are being accepted through January 11, 2021 and will be reviewed by the Air Force Research Lab Munitions Directorate’s Core Technical Competency leads as they are received. Submissions should be in Word doc or PDF format, include no more than 250 words, and one figure or image. References do not count towards the word limit.

Those selected to present will be notified more than two weeks in advance of the event. Each topic discussed will consist of an abstract presentation and a question and answer period. 

To register for this event, please do so here.

Topic 1

Novel sensing / detectionfor extreme environments 

  • novel solutions that may use non-traditional transducers that can be integrated into an additively manufactured structure.     
  • innovative technologies to enable high-speed detection of harsh environments (shock/vibration).    
  • to develop a sensing system with fast response time (<100micro seconds), high sensitivity, and shock survivability( >10,000 g) and functionality through extreme mechanical environments.  

Topic 2 

High performance materials for additive manufacturing 

  • Fundamental knowledge of the underlying metal and alloy processing-structure-property relationships in advanced manufacturing methods that can be exploited to create spatially varied compositions, phases or microstructures to produce functionally graded materials    
  • Formulation of methodologies (i.eeffective melting/solidification processes, optimized print quality vs time) to print at large scales and volumes  
  • Custom print heads capable of simultaneously blending and printing various class of materials      

Topic 3 

Machine learning and physical systems 

  • developmentmethods and techniques to overcome challenges with the limited data problem associated with machine learning.   
  • Examples can include integrating physics into the workflow through physics-informed neural networks (PINNs) in problems involving systems of partial differential equations to generate good results from sparse and noisy training data.  

Topic 4 

Machine learningand aerodynamic models 

  • the use of statistical or machine learning methodologies to combine data from both efforts into surface loads.   
  • integrated force and moment aerodynamic models for use in autopilot design workflows, kinematic trajectory analysis/optimization, and uncertainty quantification.  

Topic 5 

Correlating material structure to behavioral response in energetic materials: we seek to use machine learning to identify correlations between quantified microstructures and material response.     

Topic 6

Seeker sensors new architecture concepts and technologies 

  • newLiDARtarget characterization concepts and architectures.  
  • newLiDARlaser technology.  
  • newmachine learning and artificial intelligence to improve target detection and identification.  
  • applicationof quantum technology to the seeker sensors or possibly seeker navigation abilities.     
  • radicallyreduce cost, size, weight, and power of seeker sensors.  
  • providesensing capabilities enabling detection andidentification of targets hidden or located in a complex scene.  
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