Engineering Papers⌕ Search

Engineering topics

Harry W Jones

Publications and source records attributed to Harry W Jones.

34 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Should Oxygen, Hydrogen, and Water on the Moon Be Provided by Earth Supply, Life Support Recycling, or Regolith Mining?

The water needed for space life support has so far been provided by either direct supply from Earth on most missions or by life support wastewater recycling on the International Space Station (ISS). With the recent large reduction in launch cost, Earth resupply has become more attractive, but recycling always costs less than resupply for a large enough crew on a long enough mission. Water is also needed to make up recycling losses and oxygen and hydrogen are needed for propulsion fuel. This paper compares the costs of oxygen, hydrogen, and water on the moon for Earth supply, lunar recycling, and lunar mining. While Earth supply is the most expensive by far, lunar recycling and mining costs are not much different. Life support wastewater provides a large but limited supply, so lunar mining will always be needed. Minor cost reductions may make mining less expensive than recycling.

Harry W Jones↗
NASA NTRSDate not supplied

The Challenger Tradgedy was Caused by an Apollo Mistake, Terminating Risk Analysis

NASA’s attitude toward risk changed drastically between the Apollo and Space Shuttle design. [1]Apollo engineers were seriously alarmed about riskbecause of the fatal Apollo 1 fire and the fact that the estimated probability of another fatal accident was extremely high. The predictions were so appalling thatmanagement terminated risk analysis to avoid public apprehension. Because risk analysis was not done, the Space Shuttle design accepted excessively high andunknown risk. The immediate cause of the Challengertragedy was the mistaken decision to launch in coldweather that impaired the O-ring seals, but the fundamental cause was the high risk of the shuttle design. Before Challenger, management asserted that the probability of a fatal accident was 1 in 100,000.Probabilistic Risk Analysis (PRA) found a roughly 1 in 100 chance of a shuttle failure.

Harry W Jones↗
NASA NTRSDate not supplied

Space Habitats Should Be 1 g Shielded Space Platforms, Not on Low Gravity, Radiation Exposed Moon or Mars

Previous missions have subjected astronauts to confined space, weightlessness, and increased radiation. These impair astronaut comfort, performance, and future health. The exploration and future settlement of space will depend on the long-term presence of individual humans. This requires the development of space platforms where humans can work and live in health for many years, perhaps generations. A livable space platform must provide adequate volume, gravity, and radiation shielding. This seems easier to do in deep space or Low Earth Orbit (LEO) than on the surface of the moon or Mars. Permanently habitable deep space platforms will enable scientific and technical research, space tourism, space mission preparation, space industry development, and military surveillance and operations. The first fully habitable space station would probably be in LEO for convenience and lower cost.

Space platforms↗
NASA NTRSDate not supplied

Oversimplification of Systems Engineering Goals, Processes, and Criteria in NASA Space Life Support

This paper investigates the oversimplification of the inherently complex systems engineering process in space life support. The standard systems engineering process steps are described. The International Space Station (ISS) life support system is explained with its goals and performance criteria. Although it is not usually emphasized, the essential function of developing a hierarchy of systems and subsystems is to simplify the design process. The System Complexity Metric (SCM) shows how this di-vide-and-conquer approach also reduces the system complexity. The complete systems engineering process has many detailed steps. It is often simplified because of the effort required and the human limitations on working memory and decision span. Systems analysis demands slow, logical, and fo-cused thinking but is often bypassed in favor of quick, intuitive, subconscious “gut feel.” A study of 100 system designs found examples of 12 specific mental mistakes, such as ignoring stakeholder needs, and these mistakes are essentially oversimplifications of the systems engineering process. An analysis of space life support goals, options, criteria, and processes found 11 examples of oversimplifications in systems engineering, such as neglecting safety and cost. All these 11 oversimplifications could be traced to one or more of the 12 previously identified mental mistakes or other well-known ones, such as ig-noring sunk costs. Oversimplification of the systems engineering process is rarely noticed but is a common and harmful problem. A study of failures in 50 different space systems found that problems in systems engineering caused failures and often led to errors in design, development, and test that further contributed to failure. It seems that more diligent systems engineering could prevent many project problems and failures, but projects seem to be more guided by “gut feel” based on tradition, authority, and consensus than on the logical, rational systems engineering approach.

Simplified systems engineering↗
NASA NTRSDate not supplied

Mars Transit Life Support, Open, Closed, or Mixed?

Brief human space missions such as Apollo and shuttle used material storage to provide life support but long missions such as a trip to Mars and back are expected to use a recycling life support system similar to the one on the International Space Station (ISS). Mars transit life support design is investigated considering requirements, performance, reliability, cost, and risk. The launch cost, crew size, and reliability are variable parameters that affect the life support design choice. Greater launch cost and larger crew size tend to make recycling more cost-effective than resupply. A higher reliability requirement tends to favor resupply over recycling. A mixed system combining direct supply of minimal survival materials for very high reliability with additional materials provided by recycling systems seems the best choice.

Mars life support↗
NASA NTRSDate not supplied

Biomanufacturing in Space: BioNutrients and CO2-Based Manufacturing

Biomanufacturing can provide on-demand production of mission-critical compounds and materials to support long-duration space exploration while circumventing the challenges of transporting materials from Earth. Synthetic Biology Project is developing two biomanufacturing capabilities: BioNutrients and CO2-Based Manufacturing. BioNutrients is an ongoing mission aboard the International Space Station focused on the production of perishable nutrients in an on demand for direct for consumption. The first flight experiment of this project targeted the production of carotenoids: β-carotene and zeaxanthin, in recombinant yeast strains. Since then, the project has expanded to encompass the production of the fermented consumables like yogurt and kefir for use as a nutrient delivery mechanism. The CO2-Based Manufacturing system aims to use in situ resources to allow for biomanufacturing with minimal re-supply required. The manufacturing platform is combined with an electrochemical CO2 conversion system which can produce simple carbon substrates to support microbial based biomanufacturing. A comprehensive ground-based platform for recombinant protein purification is in development with the goal of producing a thermostable carbonic anhydrase enzyme from E. coli utilizing CO2-derived acetate. Our group hopes to propel advancements in space biomanufacturing for long duration space flight by harnessing the tools of synthetic biology.

Matthew Brian Paddock↗
NASA NTRSDate not supplied

High Reliability at Minimum Cost

This paper investigates the minimum cost of improving the reliability of complex technical systems. The two major methods to improve reliability are redesigning the system for higher reliability or providing redundant components to replace failed elements. The costs of redesign for reliability or adding redundancy are estimated. The most cost-effective combination for high reliability can be identified. The cost of increasing the intrinsic reliability of a system can be modeled as cost proportional to 1/(system failure rate) a , where the exponent “a” measures the difficulty of increasing reliability. The “a” exponent can vary from 0.25 to about 2.5. Operational reliability can also be increased by using redundant systems. The failure rate for N parallel redundant units is (system failure rate) N . The cost of redundancy is N times the system cost. The total redundant system cost is proportional to N/(system failure rate) a . The cost of redundancy increases as N gets larger, but larger N allows a higher system failure rate, which reduces the system design cost. There is a certain N, a certain level of redundancy, that has the minimum cost to achieve the required overall redundant system failure rate. The minimum cost for the redundant system is achieved at the optimum level of redundancy. The N for minimum cost is equal to -a ln (redundant system failure rate). The minimum cost of the N redundant systems is proportional to N * (original system failure rate) a . The optimum redesigned individual system failure rate is proportional to exp (-1/a), so the greater the difficulty, the higher the optimum individual system failure rate. Increasing the intrinsic reliability of a system encounters diminishing returns and at some point it becomes more cost-effective to add redundancy. The difficulty of increasing intrinsic system reliability determines the optimum design for high reliability at minimum cost.

reliability↗
NASA NTRSDate not supplied

CO2-Based Manufacturing System for Recombinant Protein Production

Space biomanufacturing is a potential In Situ Resource Utilization (ISRU) strategy to provide critical consumables and products while minimizing the launched mass for long-duration, deep space missions. On Earth, the primary biological conversion of CO 2 to biomass is through photosynthesis, and sugars from photosynthetic organisms are used as feedstocks for microbial biomanufacturing. The efficiency of non-biological reduction of CO 2 to organic molecules, such as acetate or ethanol, has greatly increased in recent years. We are designing a biomanufacturing system to rely on electrochemical CO 2 conversion products for carbon substrates to support microbial growth and production of recombinant proteins. The preliminary design includes a gas-permeable membrane bioreactor with dry salts that are rehydrated and mixed with the carbon source to support growth of bacteria or yeast. The system architecture has a partially automated bioprocessing system to concentrate biomass and purify recombinant protein. This system is designed to operate semi-autonomously with minimal crew intervention. The specific use-case scenario is to produce a thermal stable carbonic anhydrase to increase the efficiency of a proposed liquid amine CO 2 removal subsystem of an environmental control and life-support system (ECLSS) on Mars.

Biomanufacturing↗
NASA NTRSDate not supplied

Common Cause Failures Dominate and Defeat Redundancy

Common cause failures occur when several malfunctions are produced by a single event or process. They are especially damaging when they eliminate an entire set of redundant systems and disable their intended function. Redundancy is used when the individual system failure probability is unacceptably high. Redundancy can improve the overall system failure probability if the failures are independent, but the reliability gain is limited if there are dependent failures having a common cause. No amount of redundancy can reduce the total failure probability below the common cause failure probability. Common cause failures defeat redundancy. Systems with high reliability requirements often use extensive redundancy. These highly redundant systems rarely fail unless all the redundant components providing a particular function fail. Complete failures of such highly redundant systems are then usually common cause failures. Common cause failures are prevalent in highly redundant, high reliability systems. Common cause failures dominate redundancy. Redundant systems may fail due to specification, design, manufacturing, operations, or maintenance problems that disable all the identical redundant systems. Common cause failures typically account for one tenth of all failures. If the failure probability is relatively low and common cause failures are significant, adding more than two or three redundant identical units usually gives little added reliability improvement. Common cause failures can be reduced by using diverse components with different technologies and manufacturers, by separating and shielding subsystems, and by avoiding shared control, power, or location. External events and shared vulnerabilities may still cause common cause failures.

common cause failures↗
NASA NTRSDate not supplied

Four Problematic Methods in Reliability Analysis

Some basic methods used in reliability analysis are problematic because they produce incorrect and overoptimistic predictions. Initially gratifying forecasts are often invalidated by testing and operational experience. The problematic methods in reliability analysis include estimating the system failure rate as the sum of component failure rates, assuming that reliability growth continues indefinitely during testing, overestimating the benefits of redundancy, and using the fault tolerance count instead of a detailed reliability analysis. Reliability analysis can produce more optimism than accuracy. This bug may now be a feature. The optimistic bias inevitable in project planning should be corrected by realistic reliability analysis that reflects relevant experience. That the repeated poor performance of reliability analysis is found to be surprising suggests willful blindness. Rigorous methods and impartial critical review are necessary to improve reliability analysis.

Reliability analysis↗
NASA NTRSDate not supplied

Reliability Growth Modeling and Testing

Reliability growth has been modelled as an exponential decline in the cumulative failure rate that continues indefinitely as long as testing continues. Contrary to this, most reliability growth data show a brief high initial failure rate due to infant mortality followed by a long period of constant low failure rate. A two part failure rate model with an initial exponential decline followed by a constant failure rate usually fits the data and provides a more realistic description of reliability growth. The reliability growth process consists of testing, experiencing failures, finding the failure causes, and redesigning the system to remove them. The cost of reliability growth increases with the number of inherent failure modes and the time needed for them to occur and be removed. The failure modes with the lower failure rates will tend to occur later, as their Mean Time Before Failure (MTBF) is the inverse of the failure rate. Reliability growth testing has diminishing returns, since it takes longer to find and remove the less probable failures.This paper first discusses the reliability bathtub curve and then explains that reliability growth is produced by testing, identifying failure causes, and designing to remove them. A simple model of reliability growth is introduced, with a brief group of early failures followed by a constant failure rate. The cumulative failure rate n(t)/t can decline as rapidly as1/t or t-1butdeclines more slowly if additiona lfailures occur. The 56-failure Crow data seti s used to demonstrate the two-phase model of reliability growth followed by a constant failure rate. 13 additional data sets are modeled, with 9 of the 14 data sets showing reliability growth approximately as n(t)/t =1/t or t-1and substantial final failure rates. The model fits most of the data sets, but 4of the 14 show no reliability growth. The reliability growth period typically includes six failures and extends one-quarter or half the total test time. As reliability growth testing continues, the cumulative failure rate should be tracked to estimate the reliability growth exponent and the final failure rate.

reliability growth modeling↗
NASA NTRSDate not supplied

Common Cause Failures Dominate and Defeat Redundancy

Common cause failures occur when several malfunctions are produced by a single event or process. They are especially damaging when they eliminate an entire set of redundant systems and disable their intended function. Redundancy is used when the individual system failure probability is unacceptably high. Redundancy can improve the overall system failure probability if the failures are independent, but the reliability gain is limited if there are dependent failures having a common cause. No amount of redundancy can reduce the total failure probability below the common cause failure probability. Common cause failures defeat redundancy. Systems with high reliability requirements often use extensive redundancy. These highly redundant systems rarely fail unless all the redundant components providing a particular function fail. Complete failures of such highly redundant systems are then usually common cause failures. Common cause failures are prevalent in highly redundant, high reliability systems. Common cause failures dominate redundancy. Redundant systems may fail due to specification, design, manufacturing, operations, or maintenance problems that disable all the identical redundant systems. Common cause failures typically account for one tenth of all failures. If the failure probability is relatively low and common cause failures are significant, adding more than two or three redundant identical units usually gives little added reliability improvement. Common cause failures can be reduced by using diverse components with different technologies and manufacturers, by separating and shielding subsystems, and by avoiding shared control, power, or location. External events and shared vulnerabilities may still cause common cause failures.

common cause failures↗
NASA NTRSDate not supplied

Four Problematic Methods in Reliability Analysis

Some basic methods used in reliability analysis are problematic because they produce incorrect and overoptimistic predictions. Initially gratifying forecasts are often invalidated by testing and operational experience. The problematic methods in reliability analysis include estimating the system failure rate as the sum of component failure rates, assuming that reliability growth continues indefinitely during testing, overestimating the benefits of redundancy, and using the fault tolerance count instead of a detailed reliability analysis. Reliability analysis can produce more optimism than accuracy. This bug may now be a feature. The optimistic bias inevitable in project planning should be corrected by realistic reliability analysis that reflects relevant experience. That the repeated poor performance of reliability analysis is found to be surprising suggests willful blindness. Rigorous methods and impartial critical review are necessary to improve reliability analysis.

Reliability analysis↗
NASA NTRSDate not supplied

Modeling Reliability Growth

Reliability growth has been modelled as an exponential decline in the cumulative failure rate that continues indefinitely as long as testing continues. Contrary to this, most reliability growth data show a brief high initial failure rate due to infant mortality followed by a long period of constant low failure rate. A two part failure rate model with an initial exponential decline followed by a constant failure rate usually fits the data and provides a more realistic description of reliability growth. The reliability growth process consists of testing, experiencing failures, finding the failure causes, and redesigning the system to remove them. The cost of reliability growth increases with the number of inherent failure modes and the time needed for them to occur and be removed. The failure modes with the lower failure rates will tend to occur later, as their Mean Time Before Failure (MTBF) is the inverse of the failure rate. Reliability growth testing has diminishing returns, since it takes longer to find and remove the less probable failures.This paper first discusses the reliability bathtub curve and then explains that reliability growth is produced by testing, identifying failure causes, and designing to remove them. A simple model of reliability growth is introduced, with a brief group of early failures followed by a constant failure rate. The cumulative failure rate n(t)/t can decline as rapidly as1/t or t-1butdeclines more slowly if additiona lfailures occur. The 56-failure Crow data seti s used to demonstrate the two-phase model of reliability growth followed by a constant failure rate. 13 additional data sets are modeled, with 9 of the 14 data sets showing reliability growth approximately as n(t)/t =1/t or t-1and substantial final failure rates. The model fits most of the data sets, but 4of the 14 show no reliability growth. The reliability growth period typically includes six failures and extends one-quarter or half the total test time. As reliability growth testing continues, the cumulative failure rate should be tracked to estimate the reliability growth exponent and the final failure rate.

reliability growth modeling↗
NASA NTRSDate not supplied

Development of a Space Compatible Biomanufacturing System

Space biomanufacturing is a potential In Situ Resource Utilization (ISRU) strategy to provide critical consumables and products while minimizing the launched mass for long-duration, deep space missions. On Earth, the primary biological conversion of CO 2 to biomass is through photosynthesis, and sugars from photosynthetic organisms are used as feedstocks for microbial biomanufacturing. The efficiency of non-biological reduction of CO 2 to organic molecules, such as acetate or ethanol, has greatly increased in recent years. We are designing a biomanufacturing system to rely on electrochemical CO 2 conversion products for carbon substrates to support microbial growth and production of recombinant proteins. The preliminary design includes a gas-permeable membrane bioreactor with dry salts that are rehydrated and mixed with the carbon source to support growth of bacteria or yeast. The system architecture has a partially automated bioprocessing system to concentrate biomass and purify recombinant protein. This system is designed to operate semi-autonomously with minimal crew intervention. The specific use-case scenario is to produce a thermal stable carbonic anhydrase to increase the efficiency of a proposed liquid amine CO 2 removal subsystem of an environmental control and life-support system (ECLSS) on Mars.

Recombinant↗