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DOE OSTI2018

Investigation of temperature dependent microstructure evolution of pure iron during friction stir welding using liquid CO2 rapid cooling

Highlights: • Microstructure evolution during the FSW of pure iron was investigated under low and high heat input conditions, respectively. • Rapid cooling, tool stop action, marker insert and short-time annealing were employed to clarify the microstructure evolution. • Continuous and discontinuous dynamic recrystallizations occur during the microstructure evolution under both the conditions. • Incomplete and complete recrystallized microstructures were developed severally under the low and high heat input conditions. • Grain growth occurs during the normal cooling stage but it is very limited under the low heat input condition. - Abstract: The microstructure evolution of pure iron during friction stir welding was reconstructed by an ingenious experimental design, in which the rapid cooling friction stir welding combined with the tool “stop action” technique and the subsequent short-time annealing were adopted to “freeze” the transient microstructure during the stirring and reproduce the normal cooling during conventional friction stir welding, respectively. The microstructure evolution during the stirring and normal cooling was investigated along the material flow path and in the annealed “frozen” weld zone by high-resolution electron-backscatter-diffraction technique. The results show that the continuous and discontinuous dynamic recrystallizations occur simultaneously at the severe deformation stage in front of the tool both under low and high heat input conditions. However, during the material flow, the microstructure evolution involves the plastic deformation, recrystallization, high angle boundaries migration and dynamic recovery under the low heat input condition, while in a dynamic balance of deformation, recrystallization and grain growth under the high heat input conditions. At the cooling stage, normal grain growth occurs both for the low and high heat input welding conditions, while it is very limited for the low heat input condition.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Multi-degree of Freedom Energy Analysis for Identification of Failure Risk in Structural Components Subjected to Random Vibration and Shock Loading

When designing or analyzing a mechanical system, energy quantities provide insight into the severity of shock and vibration environments; however, the energy methods in the literature do not address localized behavior because energy quantities are usually computed for an entire structure. The main objective of this paper is to show how to compute the energy in the components of a mechanical system. The motivation for this work is that most systems fail functionally due to component failure, not because the primary structure was overloaded, and the ability to easily compute the spatial distribution of energy helps identify failure sensitive components. The quantity of interest is input energy. That input energy can be decoupled modally is well known. What is less appreciated is that input energy can be computed at the component level exactly, using the component effective modal mass. We show the steady state input energy can be decomposed both spatially and modally and computed using input power spectra. A numerical example illustrates the spatial and modal decomposition of input energy and its utility in identifying components at risk of damage in random vibration and shock environments. Our work shows that the modal properties of the structure and the spectral content of the input must be considered together to assess damage risk. Because input energy includes absorbed energy as well as relative kinetic energy and dissipated energy, it is the recommended energy quantity for assessing the severity for both random vibration and shock environments on a structure.

42 ENGINEERING↗

DOE OSTI2014

WE-D-BRE-07: Variance-Based Sensitivity Analysis to Quantify the Impact of Biological Uncertainties in Particle Therapy

Purpose: In particle therapy, treatment planning and evaluation are frequently based on biological models to estimate the relative biological effectiveness (RBE) or the equivalent dose in 2 Gy fractions (EQD2). In the context of the linear-quadratic model, these quantities depend on biological parameters (α, β) for ions as well as for the reference radiation and on the dose per fraction. The needed biological parameters as well as their dependency on ion species and ion energy typically are subject to large (relative) uncertainties of up to 20–40% or even more. Therefore it is necessary to estimate the resulting uncertainties in e.g. RBE or EQD2 caused by the uncertainties of the relevant input parameters. Methods: We use a variance-based sensitivity analysis (SA) approach, in which uncertainties in input parameters are modeled by random number distributions. The evaluated function is executed 10{sup 4} to 10{sup 6} times, each run with a different set of input parameters, randomly varied according to their assigned distribution. The sensitivity S is a variance-based ranking (from S = 0, no impact, to S = 1, only influential part) of the impact of input uncertainties. The SA approach is implemented for carbon ion treatment plans on 3D patient data, providing information about variations (and their origin) in RBE and EQD2. Results: The quantification enables 3D sensitivity maps, showing dependencies of RBE and EQD2 on different input uncertainties. The high number of runs allows displaying the interplay between different input uncertainties. The SA identifies input parameter combinations which result in extreme deviations of the result and the input parameter for which an uncertainty reduction is the most rewarding. Conclusion: The presented variance-based SA provides advantageous properties in terms of visualization and quantification of (biological) uncertainties and their impact. The method is very flexible, model independent, and enables a broad assessment of uncertainties. Supported by DFG grant WI 3745/1-1 and DFG cluster of excellence: Munich-Centre for Advanced Photonics.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

WORM (Write One, Read Many)

WORM (Write One, Run Many) is an easy to use, cross platform, embedded and extensible, functional programming language designed to facilitate the creation of input-decks for computer codes that use standard ASCII text files for input. WORM makes it easy to create generic (yet, complex and powerful) reusable models. Additionally its nature allows for complex calculations and routines to be coded once and easily reused, further simplifying the creation of input decks. WORM (Write One, Run Many) is a powerful and versatile tool designed to improve the efficiency of today’s criticality safety analyst by allowing: + input decks for parametric studies to be created quickly and easily, + calculations and variables to be imbedded into any input deck, thus allowing for meaningful parameter specifications, + problems to be specified using any combination of units, and + complex mathematically defined models to be created. A very simple syntax is employed, and therefore the WORM is easy to learn. A WORM model is essentially a standard input deck with some of its numerical values replaced by WORM code. WORM code may include and evaluate the following mathematical operators and functions: addition, subtraction, multiplication, division, exponentiation, modulus, sine, cosine, tangent, arcsine, arccosine, arctangent, the natural logarithm, logarithm base 10, integer truncation, absolute value, and random number. Several common constants, e.g., pi, e, and Avogadros’s Number (both as 6.022e23 and 0.6022), are predefined in WORM. Additionally, many unit conversion factors are also predefined: millimeters, meters, inches, feet, yards, and mils to centimeters; kilograms, pounds, and ounces to grams; liters, milliliters, gallons, and fluid ounces to cubic centimeters; and angular degrees to radians. For parametric studies, WORM supports various shorthand list specifications: the explicit step size, linear interpolation, and logarithmic interpolation. The list notation sequentially assigns multiple values to a name. WORM creates an input deck for each value of the name. If multiple lists are used, WORM steps through each list individually, i.e., WORM creates input decks corresponding to each and every permutation of the list values. Additionally, a library of standard material definitions and Perl subroutines are included. Any one of these files can be incorporated into the subject model with a simple WORM read command. WORM is completely written in Perl, the Practical Extraction and Reporting Language. Perl is one of the most portable programming languages available today. As such, the WORM works on practically any computer platform.

Sartor, Raymond↗

DOE OSTI2018

pnnl/Haskell-IDF-Executable

Executables for working with EnergyPlus Input Files (IDF) This project aims to provide a suite of executables for working with EnergyPlus Input Files (IDF). The project is developed using the Haskell programming language (c.f., https://www.haskell.org/). The current release (version 0.1.0.0) includes a utility that "pretty prints" the input file (i.e., that decodes and then, immediately, re-encodes and displays the input file, viz., that sanitizes the input) and a utility that converts the input file into a labeled graph in the DOT language (c.f., https://www.graphviz.org/doc/info/lang.html) for subsequent rendering using Graphviz Visualization Software (c.f., https://www.graphviz.org/). An example workflow is to pipe the input file into the pretty printer, to pipe the result into the labeled graph converter, and then to pipe the result into Graphviz, yielding an image file that depicts the hierarchical and referential relationships between EnergyPlus Data Dictionary (IDD) classes that are detected within the input file.

Borkum, Mark↗

DOE OSTI2023

Final Report On Non-Intrusive Load Monitoring Of Welding Processes

The conventional method of weld process monitoring is to monitor the process output electrical power close to the point of welding. Non-Intrusive Load Monitoring (NILM) describes the method of monitoring primary or utility electrical power into a welding process well away from the point of welding. The objective of this project was to determine if there is sufficient information within the welding process input power to understand process output power characteristics, and, if so, determine if there is sufficient resolution within the input electrical characteristics to infer some measures of weld quality. To understand the relationship between process input and output power, both input and output power were monitored for two welding processes (GMAW and GTAW) and two different power source types (inverter and SCR). Welds were made with and without intentional disturbances. The results showed that there is a strong correlation between input and output power and that the influence of process disturbances are evident within the input power. A simple method using input power only was devised and successfully demonstrated to discriminate between a weld made with no intentional disturbance (a nominal weld) from a weld made with an intentional disturbance (off-nominal weld). The primary conclusions of this work is that NILM of process input power is sensitive to process disturbances that could influence weld quality and that the approach warrants additional study.

36 MATERIALS SCIENCE↗

DOE OSTI2016

UNCERTAINTIES IN GALACTIC CHEMICAL EVOLUTION MODELS

We use a simple one-zone galactic chemical evolution model to quantify the uncertainties generated by the input parameters in numerical predictions for a galaxy with properties similar to those of the Milky Way. We compiled several studies from the literature to gather the current constraints for our simulations regarding the typical value and uncertainty of the following seven basic parameters: the lower and upper mass limits of the stellar initial mass function (IMF), the slope of the high-mass end of the stellar IMF, the slope of the delay-time distribution function of Type Ia supernovae (SNe Ia), the number of SNe Ia per M {sub ⊙} formed, the total stellar mass formed, and the final mass of gas. We derived a probability distribution function to express the range of likely values for every parameter, which were then included in a Monte Carlo code to run several hundred simulations with randomly selected input parameters. This approach enables us to analyze the predicted chemical evolution of 16 elements in a statistical manner by identifying the most probable solutions, along with their 68% and 95% confidence levels. Our results show that the overall uncertainties are shaped by several input parameters that individually contribute at different metallicities, and thus at different galactic ages. The level of uncertainty then depends on the metallicity and is different from one element to another. Among the seven input parameters considered in this work, the slope of the IMF and the number of SNe Ia are currently the two main sources of uncertainty. The thicknesses of the uncertainty bands bounded by the 68% and 95% confidence levels are generally within 0.3 and 0.6 dex, respectively. When looking at the evolution of individual elements as a function of galactic age instead of metallicity, those same thicknesses range from 0.1 to 0.6 dex for the 68% confidence levels and from 0.3 to 1.0 dex for the 95% confidence levels. The uncertainty in our chemical evolution model does not include uncertainties relating to stellar yields, star formation and merger histories, and modeling assumptions.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Uncertainty-informed selection of CMIP6 Earth System Model subsets for use in multisectoral and impact models

Earth system models (ESMs) and general circulation models (GCMs) are heavily used to provide inputs to sectoral impact and multisector dynamic models, which include representations of energy, water, land, economics, and their interactions. Therefore, representing the full range of model uncertainty, scenario uncertainty, and interannual variability that ensembles of these models capture is critical to the exploration of the future co-evolution of the integrated human–Earth system. The pre-eminent source of these ensembles has been the Coupled Model Intercomparison Project (CMIP). With more modeling centers participating in each new CMIP phase, the size of the model archive is rapidly increasing, which can be intractable for impact modelers to effectively utilize due to computational constraints and the challenges of analyzing large datasets. In this work, we present a method to select a subset of the latest phase, CMIP6, featuring models for use as inputs to a sectoral impact or multisector dynamics models, while prioritizing preservation of the range of model uncertainty, scenario uncertainty, and interannual variability in the full CMIP6 ensemble results. This method is intended to help impact modelers select climate information from the CMIP archive efficiently for use in downstream models that require global coverage of climate information. This is particularly critical for large-ensemble experiments of multisector dynamic models that may be varying additional features beyond climate inputs in a factorial design, thus putting constraints on the number of climate simulations that can be used. We focus on temperature and precipitation outputs of CMIP6 models, as these are two of the most used variables among impact models, and many other key input variables for impacts are at least correlated with one or both of temperature and precipitation (e.g., relative humidity). Besides preserving the multi-model ensemble variance characteristics, we prioritize selecting CMIP6 models in the subset that preserve the very likely distribution of equilibrium climate sensitivity values as assessed by the latest Intergovernmental Panel on Climate Change (IPCC) report. This approach could be applied to other output variables of climate models and, possibly when combined with emulators, offers a flexible framework for designing more efficient experiments on human-relevant climate impacts. It can also provide greater insight into the properties of existing CMIP6 models.

Snyder, Abigail C.↗

NASA NTRS1995

Traction Drives for Zero Stick-Slip Robots, and Reaction Free, Momentum Balanced Systems

Two differential (dual input, single output) drives (a roller-gear and a pure roller), and a momentum balanced (single input, dual output) drive (pure roller ) were designed, fabricated, and tested. The differential drives are each rated at 295 rad/sec (2800 rpm) input speed, 450 N-m (4,000 in-lbf) output torque. The momentum balanced drive is rated at 302 rad/sec (2880 rpm) input speed, and dual output torques of 434N-m (3840 in-lbf). The Dual Input Differential Roller-Gear Drive (DC-700) has a planetary roller-gear system with a reduction ratio (one input driving the output with the second input fixed) of 29.23: 1. The Dual Input Differential Roller Drive (DC-500) has a planetary roller system with a reduction ratio of approximately 24:1. Each of the differential drives features dual roller-gear or roller arrangements consisting of a sun, four first row planets, four second row planets, and a ring. The Momentum Balanced (Grounded Ring) Drive (DC-400) has a planetary roller system with a reduction ratio of 24:1 with both outputs counterrotating at equal speed. Its single roller cluster consists of a sun, five first and five second row planets, a roller cage or spider and a ring. Outputs are taken from both the roller cage and the ring which counterrotate. Test results reported for all three drives include angular and torque ripple (linearity and cogging), viscous and Coulomb friction, and forward and reverse power efficiency. Of the two differential drives, the Differential Roller Drive had better linearity and less cogging than did the Differential Roller-Gear Drive, but it had higher friction and lower efficiency (particularly at low power throughput levels). Use of full preloading rather than a variable preload system in the Differential Roller Drive assessed a heavy penalty in part load efficiency. Maximum measured efficiency (ratio of power out to power in) was 95% for the Differential Roller-Gear Drive and 86% for the Differential Roller Drive. The Momentum Balanced (Grounded Ring) Drive performed as expected kinematically. Reduction r-atios to the two counterrotating outputs (design nominal=24:1) were measured to be 23.98:1 and 24.12:1 at zero load.. At 25ONm (2200 in-lbf) output torque the ratio changed 2% due to roller creep. This drive was the smoothest of all three as determined from linearity and cogging tests, and maximum measured efficiency (ratio of power out to power in) was 95%. The disadvantages of full preloading as comvared to variable preload were apparent in this drive as in the Differential Roller Drive. Efficiencies at part load were low, but improved dramatically with increases in torque. These were consistent with friction measurements which indicated losses primarily from Coulomb friction. The initial preload level setting was low so roller slip was encountered at higher torques during testing.

Anderson, William J.↗

NASA NTRS2002

Method and Apparatus for Reducing the Vulnerability of Latches to Single Event Upsets

A delay circuit includes a first network having an input and an output node, a second network having an input and an output, the input of the second network being coupled to the output node of the first network. The first network and the second network are configured such that: a glitch at the input to the first network having a length of approximately one-half of a standard glitch time or less does not cause the voltage at the output of the second network to cross a threshold, a glitch at the input to the first network having a length of between approximately one-half and two standard glitch times causes the voltage at the output of the second network to cross the threshold for less than the length of the glitch, and a glitch at the input to the first network having a length of greater than approximately two standard glitch times causes the voltage at the output of the second network to cross the threshold for approximately the time of the glitch. The method reduces the vulnerability of a latch to single event upsets. The latch includes a gate having an input and an output and a feedback path from the output to the input of the gate. The method includes inserting a delay into the feedback path and providing a delay in the gate.

Robert L Shuler, Jr.↗

NASA NTRS2002

Method and Apparatus for Reducing the Vulnerability of Latches to Single Event Upsets

A delay circuit includes a first network having an input and an output node, a second network having an input and an output, the input of the second network being coupled to the output node of the first network. The first network and the second network are configured such that: a glitch at the input to the first network having a length of approximately one-half of a standard glitch time or less does not cause tile voltage at the output of the second network to cross a threshold, a glitch at the input to the first network having a length of between approximately one-half and two standard glitch times causes the voltage at the output of the second network to cross the threshold for less than the length of the glitch, and a glitch at the input to the first network having a length of greater than approximately two standard glitch times causes the voltage at the output of the second network to cross the threshold for approximately the time of the glitch. A method reduces the vulnerability of a latch to single event upsets. The latch includes a gate having an input and an output and a feedback path from the output to the input of the gate. The method includes inserting a delay into the feedback path and providing a delay in the gate.

Robert L Shuler Jr.↗

NASA NTRS1997

Motion video compression system with neural network having winner-take-all function

A motion video data system includes a compression system, including an image compressor, an image decompressor correlative to the image compressor having an input connected to an output of the image compressor, a feedback summing node having one input connected to an output of the image decompressor, a picture memory having an input connected to an output of the feedback summing node, apparatus for comparing an image stored in the picture memory with a received input image and deducing therefrom pixels having differences between the stored image and the received image and for retrieving from the picture memory a partial image including the pixels only and applying the partial image to another input of the feedback summing node, whereby to produce at the output of the feedback summing node an updated decompressed image, a subtraction node having one input connected to received the received image and another input connected to receive the partial image so as to generate a difference image, the image compressor having an input connected to receive the difference image whereby to produce a compressed difference image at the output of the image compressor.

Fang, Wai-Chi↗

NASA NTRS2010

Photon Counting Using Edge-Detection Algorithm

New applications such as high-datarate, photon-starved, free-space optical communications require photon counting at flux rates into gigaphoton-per-second regimes coupled with subnanosecond timing accuracy. Current single-photon detectors that are capable of handling such operating conditions are designed in an array format and produce output pulses that span multiple sample times. In order to discern one pulse from another and not to overcount the number of incoming photons, a detection algorithm must be applied to the sampled detector output pulses. As flux rates increase, the ability to implement such a detection algorithm becomes difficult within a digital processor that may reside within a field-programmable gate array (FPGA). Systems have been developed and implemented to both characterize gigahertz bandwidth single-photon detectors, as well as process photon count signals at rates into gigaphotons per second in order to implement communications links at SCPPM (serial concatenated pulse position modulation) encoded data rates exceeding 100 megabits per second with efficiencies greater than two bits per detected photon. A hardware edge-detection algorithm and corresponding signal combining and deserialization hardware were developed to meet these requirements at sample rates up to 10 GHz. The photon discriminator deserializer hardware board accepts four inputs, which allows for the ability to take inputs from a quadphoton counting detector, to support requirements for optical tracking with a reduced number of hardware components. The four inputs are hardware leading-edge detected independently. After leading-edge detection, the resultant samples are ORed together prior to deserialization. The deserialization is performed to reduce the rate at which data is passed to a digital signal processor, perhaps residing within an FPGA. The hardware implements four separate analog inputs that are connected through RF connectors. Each analog input is fed to a high-speed 1-bit comparator, which digitizes the input referenced to an adjustable threshold value. This results in four independent serial sample streams of binary 1s and 0s, which are ORed together at rates up to 10 GHz. This single serial stream is then deserialized by a factor of 16 to create 16 signal lines at a rate of 622.5 MHz or lower for input to a high-speed digital processor assembly. The new design and corresponding hardware can be employed with a quad-photon counting detector capable of handling photon rates on the order of multi-gigaphotons per second, whereas prior art was only capable of handling a single input at 1/4 the flux rate. Additionally, the hardware edge-detection algorithm has provided the ability to process 3-10 higher photon flux rates than previously possible by removing the limitation that photoncounting detector output pulses on multiple channels being ORed not overlap. Now, only the leading edges of the pulses are required to not overlap. This new photon counting digitizer hardware architecture supports a universal front end for an optical communications receiver operating at data rates from kilobits to over one gigabit per second to meet increased mission data volume requirements.

Gin, Jonathan W.↗

NASA NTRS2011

Project Management Using Modern Guidance, Navigation and Control Theory

Implementing guidance, navigation, and control (GN&C) theory principles and applying them to the human element of project management and control is not a new concept. As both the literature on the subject and the real-world applications are neither readily available nor comprehensive with regard to how such principles might be applied, this paper has been written to educate the project manager on the "laws of physics" of his or her project (not to teach a GN&C engineer how to become a project manager) and to provide an intuitive, mathematical explanation as to the control and behavior of projects. This paper will also address how the fundamental principles of modern GN&C were applied to the National Aeronautics and Space Administration's (NASA) Constellation Program (CxP) space suit project, ensuring the project was managed within cost, schedule, and budget. A project that is akin to a physical system can be modeled and managed using the same over arching principles of GN&C that would be used if that project were a complex vehicle, a complex system(s), or complex software with time-varying processes (at times nonlinear) containing multiple data inputs of varying accuracy and a range of operating points. The classic GN&C theory approach could thus be applied to small, well-defined projects; yet when working with larger, multiyear projects necessitating multiple organizational structures, numerous external influences, and a multitude of diverse resources, modern GN&C principles are required to model and manage the project. The fundamental principles of a GN&C system incorporate these basic concepts: State, Behavior, Feedback Control, Navigation, Guidance and Planning Logic systems. The State of a system defines the aspects of the system that can change over time; e.g., position, velocity, acceleration, coordinate-based attitude, and temperature, etc. The Behavior of the system focuses more on what changes are possible within the system; this is denoted in the state of the system. The behavior of a system, as captured in the system modeling, when properly done will aid in accurately predicting future system performance. The Feedback Control system understands the state and behavior of the system and uses feedback to adjust control inputs into the system. The feedback, which is the right arm of the Control system, allows change to be affected in the overall system; it therefore is important to not only correctly identify the system feedback inputs, but also the system response to the feedback inputs. The Navigation system takes multiple data inputs and based on a priori knowledge of the inputs, develops a statistically based weighting of the inputs and measurements to determine the system's state. Guidance and Planning Logic of the system, complete with an understanding of where the system is (provided by the Navigation system), will in turn determine where the system needs to be and how to get it there. With any system/project, it is critical that the objective of the system/project be clearly defined -- not only to plan but to measure performance and to aid in guiding the system or the project. The system principles discussed above, which can be and have been applied to the current CxP space suit development project, can also be mapped to real-world constituents, thus allowing project managers to apply systems theories that are well defined in engineering and mathematics to a discipline (i.e., Project Management) that historically has been based in personal experience and intuition. This mapping of GN&C theory to Project Management will, in turn, permit a direct, methodical approach to Project Management, planning and control providing a tool to help predict (and guide) performance and an understanding of the project constraints, how the project can be controlled, and the impacts to external influences and inputs. This approach, to a project manager, flows down to the three bottom-line variables of cost, schedule, and scope ando the needed control of these three variables to successfully perform and complete a project.

Hill, Terry R.↗

NASA NTRS2006

Four-Quadrant Analog Multipliers Using G4-FETs

Theoretical analysis and some experiments have shown that the silicon-on-insulator (SOI) 4-gate transistors known as G4-FETs can be used as building blocks of four-quadrant analog voltage multiplier circuits. Whereas a typical prior analog voltage multiplier contains between six and 10 transistors, it is possible to construct a superior voltage multiplier using only four G4-FETs. A G4-FET is a combination of a junction field-effect transistor (JFET) and a metal oxide/semiconductor field-effect transistor (MOSFET). It can be regarded as a single transistor having four gates, which are parts of a structure that affords high functionality by enabling the utilization of independently biased multiple inputs. The structure of a G4-FET of the type of interest here (see Figure 1) is that of a partially-depleted SOI MOSFET with two independent body contacts, one on each side of the channel. The drain current comprises of majority charge carriers flowing from one body contact to the other that is, what would otherwise be the side body contacts of the SOI MOSFET are used here as the end contacts [the drain (D) and the source (S)] of the G4-FET. What would otherwise be the source and drain of the SOI MOSFET serve, in the G4-FET, as two junction-based extra gates (JG1 and JG2), which are used to squeeze the channel via reverse-biased junctions as in a JFET. The G4-FET also includes a polysilicon top gate (G1), which plays the same role as does the gate in an accumulation-mode MOSFET. The substrate emulates a fourth MOS gate (G2). By making proper choices of G4-FET device parameters in conjunction with bias voltages and currents, one can design a circuit in which two input gate voltages (Vin1,Vin2) control the conduction characteristics of G4-FETs such that the output voltage (Vout) closely approximates a value proportional to the product of the input voltages. Figure 2 depicts two such analog multiplier circuits. In each circuit, there is the following: The input and output voltages are differential, The multiplier core consists of four G4- FETs (M1 through M4) biased by a constant current sink (Ibias), and The G4-FETs in two pairs are loaded by two identical resistors (RL), which convert a differential output current to a differential output voltage. The difference between the two circuits stems from their input and bias configurations. In each case, provided that the input voltages remain within their design ranges as determined by considerations of bias, saturation, and cutoff, then the output voltage is nominally given by Vout = kVin1Vin2, where k is a constant gain factor that depends on the design parameters and is different for the two circuits. In experimental versions of these circuits constructed using discrete G4- FETs and resistors, multiplication of voltages in all four quadrants (that is, in all four combinations of input polarities) was demonstrated, and deviations of the output voltages from linear dependence on the input voltages were found to amount to no more than a few percent. It is anticipated that in fully integrated versions of these circuits, the deviations from linearity will be made considerably smaller through better matching of devices.

Mojarradi, Mohammad↗

NASA NTRS2013

An Efficient, Highly Flexible Multi-Channel Digital Downconverter Architecture

In this innovation, a digital downconverter has been created that produces a large (16 or greater) number of output channels of smaller bandwidths. Additionally, this design has the flexibility to tune each channel independently to anywhere in the input bandwidth to cover a wide range of output bandwidths (from 32 MHz down to 1 kHz). Both the flexibility in channel frequency selection and the more than four orders of magnitude range in output bandwidths (decimation rates from 32 to 640,000) presented significant challenges to be solved. The solution involved breaking the digital downconversion process into a two-stage process. The first stage is a 2 oversampled filter bank that divides the whole input bandwidth as a real input signal into seven overlapping, contiguous channels represented with complex samples. Using the symmetry of the sine and cosine functions in a similar way to that of an FFT (fast Fourier transform), this downconversion is very efficient and gives seven channels fixed in frequency. An arbitrary number of smaller bandwidth channels can be formed from second-stage downconverters placed after the first stage of downconversion. Because of the overlapping of the first stage, there is no gap in coverage of the entire input bandwidth. The input to any of the second-stage downconverting channels has a multiplexer that chooses one of the seven wideband channels from the first stage. These second-stage downconverters take up fewer resources because they operate at lower bandwidths than doing the entire downconversion process from the input bandwidth for each independent channel. These second-stage downconverters are each independent with fine frequency control tuning, providing extreme flexibility in positioning the center frequency of a downconverted channel. Finally, these second-stage downconverters have flexible decimation factors over four orders of magnitude The algorithm was developed to run in an FPGA (field programmable gate array) at input data sampling rates of up to 1,280 MHz. The current implementation takes a 1,280-MHz real input, and first breaks it up into seven 160-MHz complex channels, each spaced 80 MHz apart. The eighth channel at baseband was not required for this implementation, and led to more optimization. Afterwards, 16 second stage narrow band channels with independently tunable center frequencies and bandwidth settings are implemented A future implementation in a larger Xilinx FPGA will hold up to 32 independent second-stage channels.

Goodhart, Charles E.↗

NASA NTRS2013

Minimal Power Latch for Single-Slope ADCs

Column-parallel analog-to-digital converters (ADCs) for imagers involve simultaneous operation of many ADCs. Single-slope ADCs are well adapted to this use because of their simplicity. Each ADC contains a comparator, comparing its input signal level to an increasing reference signal (ramp). When the ramp is equal to the input, the comparator triggers a latch that captures an encoded counter value (code). Knowing the captured code, the ramp value and hence the input signal are determined. In a column-parallel ADC, each column contains only the comparator and the latches; the ramp and code generation are shared. In conventional latch or flip-flop circuits, there is an input stage that tracks the input signal, and this stage consumes switching current every time the input changes. With many columns, many bits, and high code rates, this switching current can be substantial. It will also generate noise that may corrupt the analog signals. A latch was designed that does not track the input, and consumes power only at the instant of latching the data value. The circuit consists of two S-R (set-reset) latches, gated by the comparator. One is set by high data values and the other by low data values. The latches are cross-coupled so that the first one to set blocks the other. In order that the input data not need an inversion, which would consume power, the two latches are made in complementary polarity. This requires complementary gates from the comparator, instead of complementary data values, but the comparator only triggers once per conversion, and usually has complementary outputs to begin with. An efficient CMOS (complementary metal oxide semiconductor) implementation of this circuit is shown in the figure, where C is the comparator output, D is the data (code), and Q0 and Q1 are the outputs indicating the capture of a zero or one value. The latch for Q0 has a negative-true set signal and output, and is implemented using OR-AND-INVERT logic, while the latch for Q1 uses positive- true signals and is implemented using AND-OR-INVERT logic. In this implementation, both latches are cleared when the comparator is reset. Two redundant transistors are removed from the reset side of each latch, making for a compact layout. CMOS imagers with column-parallel ADCs have demonstrated high performance for remote sensing applications. With this latch circuit, the power consumption and noise can be further reduced. This innovation can be used in CMOS imagers and very-low-power electronics

Hancock, Bruce R.↗

NASA NTRSDate not supplied

Sensitivity Analysis of the Human Research Program’s Impact 1.0 Model

Sensitivity analysis estimates the relative contribution of the uncertainty in input values to the uncertainty of model outputs. Partial Rank Correlation Coefficient (PRCC) and Leave One Out (LOO) are methods of conducting sensitivity analysis on non-linear simulation models like the IMPACT Model. The PRCC method estimates the sensitivity using partial correlation of the ranks of the generated input values to each generated output value. The “partial” part is due to the adjustments made for the linear effects of all the other input values in the calculation of correlation between a particular input and each output. LOO removes a medical condition from test suite and calculates the change in the outcome. This is used to identify the influence of the condition input in the model The inputs to the sensitivity procedures include the number of occurrences of each of the one hundred plus IMPACT medical conditions generated over the simulations, and the IMPACT outputs from the MEDPRAT mathematical model. The outputs total task time lost (TTL), number of return to definitive care (RTDC), and number of loss of crew lives (LOCL). The IMPACT team will report the results of using PRCC and LOO on IMPACT 1.0. Tornado plots will assist in the visualization of the condition-related input sensitivities to each of the main outcomes. The outcomes of this sensitivity analysis will drive review focus by identifying conditions where changes in uncertainty, and input values could drive changes in overall model output uncertainty. These efforts are an integral part of the overall verification, validation, and credibility review of IMPACT 1.0.

Sensitivity Anaylsis↗