‘the space show’ called me E.T.

That’s the pot calling the kettle black!

Thank you for the kind words and interest! We had a BLAST at Space Access 2019!


Xplicit Computing gets discussed from 46:21 – 52:20 . Here’s the segment set to some of the presentation material:

the problem with technical blogging

Now that I think about it — there are many reasons to make no attempt. I’m still going to do it…

Following my last post ‘towards sustained hypersonic flight’, I planned to launch into the ‘thermodynamics of propulsion’. Initially I had modest ambitions, but somewhere around 20 hours of derivations and eight page of differential thermodynamics, I realized that my efforts were futile — one could just go to https://en.wikipedia.org/wiki/Propulsive_efficiency and read a more complete version with references.

I’m refocusing the work to provide my angle on the analysis, and perhaps have a few entertaining Graham-isms in there. The value being — things that are not found elsewhere. I need to establish some technical foundations so that later statements and diagrams will have merit.

This got me thinking as to what I’m up against to get the ideas and feelings across. People act on emotions. Eventually, we need action.

Here’s an example of typical accurate technical writing, in ‘robot mode’:

A typical 2nd-order finite volume scheme, expressed as single equation. You’d probably vomit if we posted the actual algorithm code here. It’s certainly elegant, but not good dinner or date conversation. Therefore it doesn’t belong on a blog and should only be discussed in a study or toilet.


Who wants to read about finite math schemes after (or during) work?

Against my hopes and dreams, I’ve learned this to be true for most readers. If you’re going to create a blog that can be enjoyed by many, rather than a few experts in the field — discussion has got to be direct and interesting — not lost in the details of mathematical schemes. Content must not ignore those who have some authority in the field…so consider the blog an entry point for other articles and papers.

Balance how to convey the main technical idea, without sacrificing readability, accuracy, or applicability to professional work!

The original Apple Phone. Talk is cheap, unless it provides enabling information….


Like patents, it comes down to: “does the disclosure enable the tech?”. There is a big gap between understanding how something works and being able to actually do it. For instance, Intel, Nvidia, AMD and other chip manufacturers expect a certain amount of free information and technical literacy in the field of semiconductors; it’s good business as it keeps customers and prospective employees engaged.

Industrial know-how is deep and guarded. Some is documented, some defined as a process, others locked inside people’s brains — a strategy to remain a valuable asset. Currently, my work online perhaps accounts for about 1% of internal scientific memos and documents. As more is curated for public consumption perhaps this figure will approach about 10%. This is to be expected for long-term high-tech R&D as the information coalesces.

For instance, technical documentation for XCOMPUTE is over 1000 pages, which does a decent job describing the structure of the code — but there is no equivalence to its dense 60,000 lines of C/C++. This is the living embodiment thousands of ideas operating in collective harmony. It was fascinating to guide its natural evolution from text document to bona fide library. Talking about it is inherently reductionist, yet we still write papers and do our best to describe key concepts and processes in a universal format.

I wish I could openly share everything…aerospace has certain inherent limitations. I have liberties on some matters…but the most important things I’m going to keep to myself. It’s a delicate act to share without helping competitors — lessons learned in recent years. I’ve been working towards a big plan for about a decade, and it will have to come out in phases as it unfolds. Lots of twists and turns!

A map of the internet, March 2019. Now that much knowledge is free and common, the really valuable stuff is off-line; specialized abilities become more rare as they deviate from this common denominator. A recent NY Times article fears that this could exacerbate socioeconomic issues.


High-quality information is readily available on long-standing websites including Wikipedia and publications out of major universities and scientific proceedings. If you’re an expert or produce new information on a topic, you probably have provided online material…millions of articles have become the new digital encyclopedic compendium.

My writing and mathematical escapades here cannot match; they’re intended to be more of a technical exploration rather than reference. There may even be math errors! It’s a waste of our time to do anything else.

It’s not that I think I’m super original, but one has hardly any chance of originality if they aren’t allowed to re-synthesize a field and make some mistakes along the way. (Of course, you’re going to need to read my papers and/or come work with me to really understand the technical approach.)

A loose approach to R&D is only appropriate in early phases: one can’t afford big mistakes in critical engineering applications. Part of the art is slowly developing a deterministic (and stable) design and analysis process that utilizes analytic, computation, and experiment to converge on design decisions. Of course as the project matures, we reference standard documents such as MMPDS and AISC to refine engineering data. Naturally, conservative estimates are used where uncertainty is high — perhaps uncertainty that outlines a lack of previous failures?


  • Success is built on top of failures
  • If you haven’t failed, you haven’t pushed hard enough.
  • Worthy things tend to be difficult, and thus require many failures.
Dunning-Kruger Effect with superimposed population densities: We’ve all been there. Well, most of us… the black line is the “wisdom-confidence curve” showing that inexperienced persons tend to think they know a lot. After doing some stupid shit on the peak of Mt Stupid, they plunge into the valley of despair. It’s a long climb to gain all that knowledge and eventually confidence returns. The colored lines are log-normal population densities for varying distribution widths. The red line is a proficient homogeneous workforce. The green line is a bit more diverse with more gurus and more idiots. The blue is a widely-diverse population with a few more geniuses but at the cost of many more ass-hats. You know, like employees who drive box trucks into doors or bridges.

Everyone has an opinion — and now we can voice it online without social accountability! Further, with Google search we can easily find the “facts”.

We all can talk about causality and professional judgment (in retrospect), but few are apt at practicing and managing the inherent risks when there are many competing real factors at play. The process becomes somewhat of a personal art pulling from a myriad of experiences. Mastering this art often requires major general tribulations and experience that cannot be emulated by AI or a novice. Even the best had to crawl through a lot of pain and anguish, and I think those who don’t settle continuously find themselves at odds with the status quo.

It’s important to note that there is no objective authority in science as to who is correct and who is wrong! Although, certain institutions and individuals certainly lead in credibility — but that should remain challenged. Even when it comes to the Standard Model, there is room for improvement. Therefore it’s imperative to not just explore “local optimizations in knowledge”, but to understand its underlying principles enabling one to extrapolate beyond the well-known.

I guess I’m saying: I’m not really the kind of engineer or scientist to shoot from the hip. However as you try to do more ambitious things, more situations require it — with tamed composure. I’m old-school; I wish everything could be solved with analytical closed-form solutions. I’ve since also experienced the beauty and power of computation — an emerging pillar of science. However at the end of the day, none of that means anything if the experiment or test data says otherwise.

The challenge is not doing it; it is doing it well…or better than before.

coming soon — adventures in tech

Welcome to my new blog!

I hope to share some fun stuff in the realm of numerical simulation, machine design, and aerospace systems. Realistically, lots of other crazy stuff will pop up along the way….

A bit about me: educated at Harvey Mudd College in general engineering. Had a few internships at NASA JPL/Caltech supporting Dawn, MSL, and hypervelocity impactor programs. Then a short stint at Blue Origin developing engine infrastructure. Found myself reinventing CFD numerics on my laptop in Matlab to address engine and facility challenges at Virgin Galactic. Then founded Xplicit Computing and worked very hard to bring all the best ideas and people together. Broke code and new ground in HPC…

Over the last five years I’ve been very focused on building the software data layers to enable next-gen engines and power systems. XCOMPUTE enables us to define and simulate complex systems building blocks for heterogeneous (CPU/GPU) algorithm processing. This means we can solve fluid, solid, or any other problem in a unified architecture, leveraging the latest in C++ and OpenCL. Powerful advanced simulation is now available on desktop computers! Computing tools are now accessible to many more people…a huge impact on small and big businesses.

I’m also into a variety of music (piano improv, percussion, etc), cultural foods (many types), and philosophy (Spinoza-Einstein).

Very exciting new things on the horizon. Stay tuned…