JuanCastillo29/space-mission-intelligence-data huggingface dataset:
( )
( )
⎠ ⎟ Vsm +ΔVt
Eq. 22
⎝ ⎜
dt Where:
dm/dt = Mass Flow Rate of media through thruster (kg/s) Vsm = Velocity of spacecraft through media (m/s) ∆Vt = Delta V of media by thrusting effect (m/s)

And where:
€
⎛
⎞
F = dm
⎠ ⎟ ΔVt Eq. 23
⎝ ⎜
dt In the case of interacting with the interstellar medium, during the dominant part of the trajectory, the speed of the spacecraft through the media, Vsm, is much higher than the delta V that the thrusting effect can impart to a portion of that media, ∆Vt (Vsm >> ∆Vt). In that case, the power equation can be approximated by the much simpler form which illuminates the fundamental truth of all such propulsion—that the faster the spacecraft, the higher the power requirements:
€
P ≈F × Vsm Eq. 24
For acceleration using plasma reaction, Propulsion System Specific Power (Psp) of these propulsion systems is still very important; usually this is dominated by the power supply carried with the spacecraft. Because Type IP-XM systems are not limited by their stored reaction mass, the energy content of the power supply is what ultimately controls the ∆V available.
€ ΔV = F
Ta Eq. 25
M sl Where:
∆V = Change in velocity imparted to the spacecraft, (m/s) Msl = Spacecraft Launch Mass (= Payload + Spacecraft Empty Mass), (kg) Ta = Acceleration Duration (s)
€ This applies both to thrust and drag devices—however, while available power supplies tend to have low Psp and hence low accelerations, some drag devices offer high decelerations because they are dissipating power in to the interstellar plasma rather than consuming it. Drag devices may be power-limited (in which case they can be modeled as thrust devices), or they may have a constant “ballistic coefficient” like a parachute, in which case their drag varies with the square of the velocity through the medium, 𝑉sm.
In the case where Vsm is high, one also must check whether thrust, F, needs to be replaced by a "net thrust" or thrust minus drag. It may seem counterintuitive that one would be concerned with “drag” in the thin interstellar medium, but devices to collect reaction mass, almost by definition, have some way of interacting with the interstellar medium and so do offer drag. Neglecting this led to some early over-estimates of performance of some types of Bussard ramjets, for example. While the means of estimating drag is rather specific to the particular device in question, it is usually sufficient for comparison purposes to check if it is significant at the speeds in question (it often is not), and to account for it only in cases where this is not so.
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Millis 2018 Grant NNX17AE81G_for_CR.docx Concepts like negative mass propulsion, the Mach Effect Thruster, and the warp drive all face the challenge of an ambiguous reaction mass.
Regardless of those specifics, the techniques to estimate their performance can be crudely estimated in terms of converting stored energy into kinetic energy of the spacecraft, with some conversion efficiency, η, and a Propulsion System Specific Power, Psp, (W/kg). Absent of better values, comparable efficiencies and specific powers from other concepts can be considered as a starting point.
For the special case of the warp drive, energy conversion equations exist [13 p.491, 103], but there are no equations yet that remotely resemble Propulsion System Specific Power.
The timescales for interstellar missions are comparable to prior technological revolutions (figure 3). Thus it is entirely possible that a revolutionary technology will emerge and surpass the performance of a more evolutionary technology already in development. But how does one predict if, and when, that might happen and what to do about it?
Recall that the objective of this study is not just to reveal which propulsion concepts might be the most advantageous (and under which circumstances), but also to identify the most impactive supporting technologies to guide the selection of a prudent portfolio of next-step research. This requires developing methods to estimate, not only the impact of a particular technology, but also estimate when that technology might be ready for mission commitment.
While it is not possible to predict the future, technology developments do follow patterns that can be used as a guide. First, there are the "Technology Readiness Levels (TRL)," that are both a way to assess the readiness of a given technology as well as identifying what further steps are needed to advance that technology to mission readiness. Second, there is the "S-curve" pattern to technological improvements and revolutions that suggest how to model that process [41]. Another source under consideration is the "Technology Forecasting and Readiness Assessment" methods of Darryl Web, et al [104].
There are at least two distinctly different rates of advancements to model: 1) where the performance values of mission-ready (TRL ≥ 6) technology improve over the years, and 2) where potential increases in performance levels advance up the TRL scales, from concept to mission readiness. That first type, advancing performance over time, pertains mostly to baseline technologies shared by the different propulsion of power concepts (such as payload miniaturization). That second type, the maturation of new performance abilities, pertains more to the propulsion and power concepts being assessed.
All of these assessments will be relative rather than absolute. In other words, the assessment will judge if one technology might reach fruition before another, but will not be able to accurately predict the actual time when either will reach fruition. The key here, is that all the competing methods are compared to the same standards.
Caveat: At this time, none of these predictive tools have been completed and tested. This is an area where much will be learned in the attempt, but predicting the future, even in relative terms, carries with it a great deal of uncertainty. Absent of any alternatives, however, these methods are at least a starting point.
To equitably compare different propulsion and power concepts, it is necessary that the parameters that they share in common be set to the same, baseline values. As previously discussed, identical payload and ∆V requirements are imposed when comparing different propulsion concepts. In addition, this is extended here to include common subsystem technologies that are the same across different propulsion methods, such as thermal radiators, tankage fraction (for the same propellant-based systems), and energy density for systems using the same energy storage technology.
These baseline values, however, will change over the years. To compare the overall performance of missions begun in different years, the rates and limits of these trends will have to be modeled. For consistency with the definition of "Mission Commitment Year, Ym," described in Section 7.3, the values estimated for each year shall be those that have reached TRL ≥ 6. Table 16 shows an example of how this tracking might begin. Note that the last column is labeled "physics limit." Trends can hit a limit. Miniaturization will reach a point where the number of
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pg 53 of 69 molecules per function cannot be further reduced.

An example of a real propulsion limit is the maximum theoretical Isp of hydrogen-oxygen rockets, which is 528.5 sec Isp, (based on 100% conversion of stored energy into kinetic energy and a propellant specific energy of 13.43 MJ/kg).
It is expected that these trends can be modeled by exponential equations or nested S-curves (Section 8.5.3). To guide those equations, data on the performance level changes over the years and any limits (to set asymptotes) is required.

| Trends to Model Performance levels of TRL-6 technology | 2020 | 2030 | 2040 | ... | Physics Limit |
| --- | --- | --- | --- | --- | --- |
| Available Mission Energy (J/y) (see table 10.) | 5E+14 ? | 6E+14 ? | 8E+14 ? |  | none? |
| Payload Mass (by Miniaturization) (kg/function) | 30 kg ? |  | 1 g ? |  | ? |
| Shared Design Standards |  |  |  |  |  |
| Power Source Power Density (W/kg) | 0.5 kw/kg ? |  |  |  |  |
| Energy Storage Density (J/kg) |  |  |  |  | c2 |
| Tankage Fraction (for a given propellant) | 3.7% (STS ET) |  |  |  |  |
| Radiator specific mass (kg/W) |  |  |  |  |  |
| Etc. |  |  |  |  |  |

Related to these are the advancement rates for infrastructure readiness. This was already discussed in Sections 7.3 and 8.3. and repeated here for contextual reference.
a new power or propulsion mechanism based on established effects |  |
| 1 |  |  |
| SRL-4 | New effect observed and reported that is relevant to power or propulsion goals |  |
| 3 |  |  |
| SRL-3 | Experimental test devised for an effect relevant to power or propulsion |  |
| 1 |  |  |
| SRL-2 | Hypothesis devised for an effect relevant to power or propulsion |  |
| 3 |  |  |
| SRL-1 | Problem statement devised for an effect relevant to power or propulsion |  |

To create these estimates, it would be helpful to examine the histories of key parameters like those listed in table 12. In addition to plotting performance steps over time, it would be helpful to know the resources expended to attain each improvement.
There are more influences on advancement rate than just the ratio of TRL steps. If a concept does not have enough history (or inconsistent history or resources and commitment), then the following additional factors require consideration:
• Level of available resources—where greater resources lead to faster progress • Operating power of concept—where lower power devices are easier (faster) to advance • Complexity of concept—where simpler devices are easier (faster) to advance • Relation to S-curve—where the rate of progress is fastest when midway between the performance level of the first embodiment to the level of its upper physics limit
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That last item, the "S-curve," is the subject of the next subsection.
Technological advancements follow recurring patterns. One of those patterns is the "S-curve" of technology advancement as described by Foster [41].
The S-curve evolution, shown in figure 7, is typical of any successful technology. The pattern begins where the initial efforts produce little improvement until a breakthrough is reached. The breakthrough, at the lower knee of the curve, is where the technology has finally demonstrated its fidelity. After this point significant progress is made with ever-improved versions of the same concept. Eventually, however, the physical limits of the technology are reached and continued efforts result in little additional advancement. This upper plateau is "the point of diminishing returns." To go beyond these limits a new alternative technology, with its own S-curve, must be created. Examples include how jet aircraft surpasses the speed limits of propeller aircraft, or how steam ships surpassed the performance of sailing ships. Note, the horizontal axis is not time as expected, but rather the resources ($ and time) devoted to the research.
•  Each method follows a similar development curve, eventually reaching a limit.
•  To surpass that method, a different method is created which has its own curve
Next Method
Performance
Theoretical Limit Prior Method
Resources

[Figure: Figure 7.  S-Curve Pattern of Technology Advancement]

Foster (1986) Innovation - The Attacker’s Advantage, 1986
There are multiple lessons that that go along with this recurring pattern: • The appearance of long-term exponential growth in performance is actually a series of S-curves • Advancement rates vary depending on how mature the concept • Indicators exist for when it is appropriate to seek new S-curves ("breakthroughs") • This pattern is mathematically describable by a "Logistic Function" Exponential Growth: Sometimes it appears that performance gains follow an exponential growth, such as "Moore's law." Moore's law is the observation (and subsequent extrapolation) that the number of transistors in an integrated circuit double about every two years. While this trend might hold for a while, eventually a limit is reached. For example in the extreme, is it safe to assume that transistors can never get smaller than a single subatomic particle? Further, it has also been shown that these apparently exponential growths in performance are actually a sequence of overlaid technology "S-curve" advancements [106].
Changing Advancement Rates: Note from figure 7 that the rate of performance gains, verses resources applied, is the greatest when a given technology is at the midpoint of its development. As expected, early progress is slow. What is often overlooked with well established technologies (ones near the top of their S-curve) is that additional performance gains get increasingly more costly to obtain and reach a point where no further performance gains are possible. An example of this is the history of LH2 rocket engines, where its theoretical limit is 528.5 sec Isp, (based on 100% conversion of stored energy into kinetic energy and a propellant specific energy of 13.43 MJ/kg) and the Space Shuttle Main Engines achieved 452 sec Isp, after considerable investment.
Thus, when considering the rates of future advancement of a technology if using the TRL step analogy, then that rate will become slower for technological concepts that are approaching their upper theoretical operating limits.
Indicators for Emerging Breakthroughs: History has repeatedly shown that the conditions are ripe for breakthrough technologies to emerge when the following two conditions have been met: 1) The prior technology is reaching the point of diminishing returns, and 2) Clues have emerged for how to seek those breakthroughs (that it's
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Millis 2018 Grant NNX17AE81G_for_CR.docx not just science fiction any more).
After this study was well into it's third quarter, another set of technology advancement predictions, by Darryl Webb, were encountered. These will be examined in the next stage. Webb's method dissects the stages into other terms where advancement rates were inferred. An example of the comparison of Web's "Technology Maturity Status (TMS)" to the TRLs, and "Manufacturing Readiness Level (MRL)" is shown in table 14 (Reprinted with permission). This also shows how the TRL definitions have evolved over time.
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[Table: Table 14, Alternative Progress Measures, Linking Performance, Schedule, and Cost] [Table 1 from Webb, AIAA 2014-4483, with permission]

Millis 2018 Grant NNX17AE81G_for_CR.docx Using the propulsion comparison methods outlined in Section 8.4, trial data plots are offered next.
The intent is both to test the analysis methods and show how to present their findings in a comprehendible manner. The charts are merely for illustrative purposes, with notional missions and representative technologies to give some sense of the trends and what the data will look like when the charts are based on more complete data in Stage II.
To make the complicated trade space of interstellar flight more comprehensible, it is important to make the interpretation of the data “as simple as possible, but no simpler.” To that end, the basic figures of merit are plotted; distance, time, and energy. Though these can be shown as 3D plots with lines representing the performance levels of different technologies, the charts offered here are slices through that 3D trade space, at three different distances:
1) Solar Gravitational Lens Mission, > 660 AU (0.1 x 1015 m, 0.01 ly) 2) Deep Interstellar Medium, > 27,000 AU (4.1 x 1015 m, 0.43 ly) 3) Centauri Flyby, > 270,000 AU (41 x 1015 m, 4.3 ly) Note that these distances match the first three baseline mission & payload scenarios described in Section 8.2. Only the flyby missions are plotted at this time because there is not yet enough data for the slow-down or orbiter missions to create useful illustrations.
The axes of these 2D slices are fundamentally energy and time for various mission distances, showing five data points that represent the performance of five hypothetical propulsion concepts. Those five hypothetical concepts include a chemical rocket, a basic nuclear rocket, a higher thrust fusion rocket, a laser sail, and a sailbeam. These provisional charts deliberately do not identify which data point is associated with each method, since the values for those methods are arbitrary test cases at this time.
Two sets of plots for each of these three different distances are offered, one that is more in terms of comparing technologies, and the other more in terms of comparing mission performance with those technologies.
The "Technology Comparisons" essentially show how quickly a payload mass can be delivered to a certain distance per an energy cost. More specifically, the trip time and its associated payload delivery capacity is shown for the five hypothetical propulsion concepts.
For the time axis, it was found that a more effective use of chart space is to plot that axis in terms of "Excess trip time" instead of "trip time." “Excess trip time” is defined as the trip time minus the lightspeed flight time (where lightspeed flight time is distance ÷ lightspeed). For the long trip times of interstellar flight this technique is simply a way to make better use of the logarithmic chart space, in effect spreading out the points that would otherwise tend to cluster.
To convey the payload delivery capacity, it is helpful to speak in terms of the “specific equivalent energy.” The specific equivalent energy is really a figure of merit for cost, in which elements such as capital cost of ground infrastructure are converted into energy terms (using the conversion factor of 60 MegaJoule/dollar for bulk electricity, see Section 8.3.1). The plots show the reciprocal of that figure (kg/J), so that superior technologies are at the top of the chart. In other words, the more mass delivered per Joule, the better.
The technology charts also include a “conventional limit” line.  This essentially represents a 100% conversion of supplied energy into the kinetic energy of the spacecraft. Note, since this includes an energy cost factor (“wall plug” price of power, 60 MJ/dollar) this is not a hard physics limit, but a limit that might be able to be surpassed depending on the cost of energy. For example, technologies which harvest energy from ambient sources, or which employ forms of energy which are significantly cheaper than the “wall plug” price, can in principle supersede that limit. The line illustrates the ideal performance zone on the chart, the upper right corner. The more desirable approaches are fast (right side of the chart) and deliver more payload per energy (top of the chart).  In other words, shorter trips using less energy.
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(8a) 660 AU (SGL) (8b) 27,000 AU
(8c) 270,000 AU (Centauri)

[Figure: Figure 8 Technology Comparison Plots (time, kg/J)]
The "Mission Comparisons" essentially plot mission duration verses energy. More specifically, the combined time for the trip and signal return are compared to the propulsive energy expended, for each of the five hypothetical propulsion concepts.
On the mission charts, the time axis is a different "Excess Time," one that now accounts for the time for the data signal to reach Earth. Specifically, the excess time on the mission charts is defined as the trip time minus twice the distance ÷ lightspeed. Again, this helps spread out the data of the different propulsion performances so they can be more easily distinguished.
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Millis 2018 Grant NNX17AE81G_for_CR.docx In the case of the mission charts, the z axis is also slightly different.

Instead of plotting kg/J, the axis is just in terms of energy. To retain the same sense of “good” missions being at the top right side of the chart, the z-axis is the reciprocal of total energy (1/J) instead of energy.
(9a) 660 AU (SGL) (9b) 27,000 AU
(9c) 270,000 AU (Centauri)

[Figure: Figure 9 Mission Comparison Plots (time, kg/J)]

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The Stage II work will focus on the collection and display of the information needed to conduct these analyses and then to begin the analyses. Much of this information will be openly accessible via an online repository. Access to the analysis tools themselves, where the user can vary the parameters to assess the consequences, might be more limited. The information portals (or front ends), fall into these categories:
• Portal where subject matter experts check, and add to, the online repository of information • Analysis portal, where select operators can vary the mission choices and technology prospects to assess the results. • Distilled summaries of mission, prospects, and findings; suitable for general audiences
In addition to the span analyses to be conducted offline, it is desired to create a limited-access site with a partial analysis tool, where key options are available as 'sliders' and the outputs are in easily understandable graphics. Key challenges here are on creating an effective user interface and comprehensible graphs of the results. By attempting this key-factors front end, it may help improve the design of the deeper level of details for selecting analysis inputs and selecting how to convey the results.
The analysis inputs will include key mission choices (Section 7.) in a way that follows the "Basic Analysis Flow Diagram" of figure 5. As a starting point to envision the outputs of the analyses, consider both the sample "Topological Maps" from figure 1 and the data plots from Section 9.
Any challenges to the assumptions and initial estimates in the report are welcome. While the focus of the first stage work was the development of the overall structure for the data to be subsequently collected in later phases, some values have been specified as starting estimates, for which more accurate and defensible numbers are sought. The online repository is the envisioned mechanism to keep these values up to date. Prior to that system being available, readers who have more accurate numbers for any of these values, please contact the authors with that information along with a reference citation for those more accurate values. Further note that most values herein are specified to only about two significant digits—consistent with the current fidelity of interstellar flight estimates.
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