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Development of Weigh-Out Process and Evaluations for Underwater Partial Gravity Simulations

For the upcoming Artemis lunar missions, astronauts will need to train in a spacesuit where partial gravity can be simulated such as the NASA Neutral Buoyancy Lab (NBL). At the NBL, dive weights and foam can be added around the spacesuit to attain a satisfactory center of buoyancy (CB) and center of gravity (CG) location to simulate the lunar gravity (1/6thG) effects. If CG and CB are not co-located properly, incorrect righting moments can be introduced, and both simulation quality and EVA task performance can be impaired. Based on the findings from the initial testing using xEMU spacesuits, it was observed that the weigh-out method (i.e., determination of the weights and foam quantities and position) needed further development to improve the simulation quality, especially for the subjects who experienced excessive instability. This paper aims to present the on-going effort to improve the weigh-out process for enabling NBL lunar EVA simulations. For this effort, a human-suit model was created to use suit CAD and 3D human body scans to estimate both CB and CG location for each suited subject. NBL weigh-out testing was performed to characterize the effects of CG and CB positioning, in which the 3D human-suit model was used to determine optimal weigh-out combinations of weights and foam. Postural, balance, and subjective feedback were gathered for each weigh-out configuration. The results indicated that, as the CB was shifted higher and the CB and CG located closer to each other, the subject tended to be more stable and their EVA performance improved. A high CB location was then prioritized across 4 additional subjects in both small and large size spacesuits. When compared to the initial xEMU test series, improved performance was observed across all subjects as the CB moved higher and aligned closer to the system CG.

Pouyan Sabahi↗
NASA NTRS2023

CFD Modeling of Bi-Directional PMD inside Cryogenic Propellant Tanks Onboard Parabolic Flights

Future cryogenic propulsion systems will require efficient methods with which to transfer cryogenic propellants from a depot storage tank to a customer receiver tank to minimize cost and maximize reusability. The Reduced Gravity Cryogenic Transfer project is currently developing advanced cryogenic fluid management technology and developing and validating new numerical models for three phases of transfer: line chilldown, tank chilldown, and tank fill. Additionally, multiple liquid nitrogen (LN 2 ) parabolic flight transfer rigs are being designed by universities and NASA to investigate the gravitational sensitivities that exist in these three technologies. In order to maximize the collection of low-g data during flights, it is required to extract as much (LN 2 as possible from the supply tank, despite variable gravity levels. The purpose of this paper is to present computational fluid dynamics (CFD) volume of fluid simulations of (LN 2 behavior in the supply tank onboard parabolic flights to validate the optimal design of a bi-directional propellant management device (PMD) using the commercial software FLOW-3D. A parametric study is conducted on the effects of gravity level, fill level, pore size, open area, thickness, and type of baffle on PMD performance. Based on results, the PMD as designed exceeds the targeted expulsion efficiency.

Jason Hartwig↗
DOE OSTI2018

The EGS Collab Hydroshear Experiment at the Sanford Underground Research Facility – Siting Criteria and Evaluation of Candidate Sites

The objective of the EGS Collab project is to establish a suite of intermediate-scale (~10-20 m) field test beds coupled with stimulation and interwell flow tests to provide a basis to better understand fracture stimulation methods, resulting fracture geometries, and processes that control heat transfer between rock and stimulated fractures. Experiment 1 of the project is being conducted at a depth of ~1.5 km in the Sanford Underground Research Facility (SURF) on the 4850 Level (ft. below the ground surface). The stimulation method planned for Experiment 2 of this project is hydroshearing of an existing natural fracture. In siting this experiment, there are several key geologic criteria that need to be met. These include: 1) the fracture should be least 10 m in length so that it could be intersected by two boreholes that are spaced that far apart; 2) it should be optimally oriented relevant to the stress field so that it is critically stressed; 3) the site should have appropriate stress conditions (not too shallow a depth); 4) the fracture should have sufficient permeability to allow for prestimulation flow testing, but not too high a permeability that would preclude permeability enhancement via shear stimulation; 5) the fracture should not intersect other features (permeable fractures, boreholes, adjacent drifts) that could serve as major leak-off zones; 6) the site should not be complicated by geology (a single lithology would be preferred). In addition, there are logistical criteria that will also influence site selection, including: 1) site availability/access, 2) logistical support, e.g., power, internet, water, operating rail system; 3) drift size and orientation compatible with drilling and site operations; 4) appropriate ground support. There are only two deep levels at SURF that can be accessed by the Yates Shaft: the 4100 and the 4850, so we limited our search to these two levels. Currently, three candidate sites are being evaluated: the existing Experiment 1 site and its surroundings on the West Drift on the 4850 level, and two locations on the 4100 level. This paper will evaluate advantages, disadvantages, and risks associated with these three options at SURF.

EGS Collab, Sanford Underground Research Facility↗
DOE OSTI2021

Scalable Techniques for Stochastic Power Flow Problems (Final Report)

The proposed research focuses on developing scalable algorithms for two-stage security-constrained OPF problems with AC power flow constraints, a class of problems complicated by (i) scale arising from a scenario representation; and (ii) the presence of nonlinearity, nonconvexity, and possibly second-stage discreteness or complementarity. Unfortunately, most existing solvers cannot contend with both challenges simultaneously; accordingly, the proposed research focuses on developing solution techniques that can both scale with the number of scenarios and contend with nonconvexity and second-stage complementarity. We consider three avenues for addressing such problems: (i) Variable sample-size SQP (VS-SQP) methods that combine sparse Quasi-Newton updates with a scalable variance-reduced stochastic gradient scheme for stochastic QP subproblems, allowing for contending with second-stage complementarity via regularization; (ii) Variable sample-size stochastic Interior-point (VS-sIP) schemes that propose a sampling-based regularized (to allow for contending with complementarity) interior-point schemes in which a Schur-complement technique is employed for decomposing the Newton direction computation step; (iii) Variable sample-size tractable ADMM (VS-tADMM) schemes combine variable sample-sizes with carefully designed techniques for resolving each of the nonconvex updates (by leveraging the QCQP structures). We intend to compare the three schemes using performance profiles in terms of solution quality, scalability, etc. and then select one scheme which will then be developed and further refined in Python for purposes of the GO competition.

42 ENGINEERING↗
DOE OSTI2019

Automated Quantitative Analysis of Extended Irradiation Defects - Dislocations, Voids and Precipitates in Neutron Irradiated HT-9 Steel

Structural alloys that make up the core components of nuclear reactors are exposed to radiation that can substantially alter the microstructure and mechanical properties of the material. Irradiation can cause the formation of extended defects including dislocation line and loops, voids and various types of precipitates. To understand the irradiation effects, it is critical to characterize these defects individually and collectively to quantify how they evolve throughout irradiation stages. Transmission electron microscopy (TEM) is a standard tool for providing extensive size, number density and spatial distribution of extended irradiation defects, however, manual measurement of defect size and number density requires time-intensive effort to analyze enough defects that provide a statistically meaningful result and are prone to errors of subjectivity. In this work, we developed a set of MATLAB algorithms offering robust and automated identification and quantification of extended irradiation defects like dislocation lines, voids and precipitates commonly observed in neutron irradiated cladding alloys [1]. Using a neutron-irradiated HT-9 martensitic alloy irradiated at 412°C to 111.8 dpa, we demonstrate the capability of these algorithms for quantitative defect analysis. Figure 1 presents defect images of the HT-9 using an advanced diffraction contrast imaging scanning transmission electron microscopy (DCI STEM) that suppresses bend contours and provides better defect contrast compared to conventional TEM [2]. The first step of defect analysis is defect identification, a process that can be digitally achieved by contrast thresholding (for simple defect contrast) and by machine learning computer vision for defect semantic segmentation [3]. Here, labeled binary defect images (Figures 2a and 2c) were used. For dislocation density quantification, we employed an optimized circular-grid intersection method first introduced by Dr. John Steeds [4]. This method is shown in Figures 2a and 2b, where the number of intersections of dislocation lines with circular traces was measured. This produces an unbiased sampling of dislocation lines, regardless of their orientation. This process was optimized with automated digital intersection number and grid length registration. Interestingly, it was found that a relatively large number of circles in a set circular gird is was needed to produce an invariant result (Figure 2b), with a low systematic measurement error. This demonstrates the main advantage of computer-based defect analysis, in performing repetitive operations quickly with a reduced systematic error. For the quantitative analysis of voids and precipitates a challenge encountered with automated defect analysis is feature overlap, particularly when the density of voids/precipitates is high. To mitigate this problem, as shown in Figure 2d, we modified the popular circular Hough transformation and treated ‘circular shape’ as the particle descriptor for voids/precipitates, efficiently resolving the overlapping particles. In summary, this automated digital quantification together with machine learning enabled defect identification [3] opens new opportunities for standardizing the characterization workflow for quantitative analysis of extended irradiation defects.

Sainju, Rajat↗
NASA NTRS2020

Interpreting Exo-Planetary Atmospheres with the Next Generation Space-Based Telescope

We will soon have the technological capability to measure the atmospheric composition of temperate Earth-sized planets orbiting nearby stars. Interpreting these atmospheric signals poses a new challenge to planetary science. In contrast to jovian-like atmospheres, whose bulk compositions consist of hydrogen and helium, terrestrial planet atmospheres are likely comprised of high mean molecular weight secondary atmospheres, which have gone through a high degree of evolution. In order to understand the processes which affect a planetary atmosphere, I use early Mars as a case study. I leverage a combination of one-dimensional climate, photochemical and energy balance models in order to create one self-consistent model that closely matches currently available climate data. However, unlike in-situ observations from our own solar system, remote sensing techniques need to be developed and understood in order to accurately characterize exo-atmospheres. I describe the models used to create synthetic transit transmission observations, which includes models of transit spectroscopy and instrumental noise. Using these, I lay the framework for an information content-based approach to optimize our observations and maximize the retrievable information from exo-atmospheres. First I test the method on observing strategies of the well-studied, low-mean-molecular weight atmospheres of warm-Neptunes and hot Jupiters. Upon verifying the methodology, I finally address optimal observing strategies for temperate, high-mean-molecular weight atmospheres (Earths/super-Earths).

Batalha, Natasha↗
DOE OSTI2021

Dynamical Large Deviations of Two-Dimensional Kinetically Constrained Models Using a Neural-Network State Ansatz

We use a neural-network ansatz originally designed for the variational optimization of quantum systems to study dynamical large deviations in classical ones. We use recurrent neural networks to describe the large deviations of the dynamical activity of model glasses, kinetically constrained models in two dimensions. We present the first finite size-scaling analysis of the large-deviation functions of the two-dimensional Fredrickson-Andersen model, and explore the spatial structure of the high-activity sector of the South-or-East model. These results provide a new route to the study of dynamical large-deviation functions, and highlight the broad applicability of the neural-network state ansatz across domains in physics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2008

Estimation of Supersonic Stage Separation Aerodynamics of Winged-Body Launch Vehicles Using Response Surface Methods

A collection of statistical and mathematical techniques referred to as response surface methodology was used to estimate the longitudinal stage separation aerodynamic characteristics of a generic, bimese, winged multi-stage launch vehicle configuration using data obtained on small-scale models at supersonic speeds in the NASA Langley Research Center Unitary Plan Wind Tunnel. The simulated Mach 3 staging was dominated by multiple shock wave interactions between the orbiter and booster vehicles throughout the relative spatial locations of interest. This motivated a partitioning of the overall inference space into several contiguous regions within which the separation aerodynamics were presumed to be well-behaved and estimable using cuboidal and spherical central composite designs capable of fitting full second-order response functions. The primary goal was to approximate the underlying overall aerodynamic response surfaces of the booster vehicle in belly-to-belly proximity to the orbiter vehicle using relatively simple, lower-order polynomial functions that were piecewise-continuous across the full independent variable ranges of interest. The quality of fit and prediction capabilities of the empirical models were assessed in detail, and the issue of subspace boundary discontinuities was addressed. The potential benefits of augmenting the central composite designs to full third order using computer-generated D-optimality criteria were also evaluated. The usefulness of central composite designs, the subspace sizing, and the practicality of fitting low-order response functions over a partitioned inference space dominated by highly nonlinear and possibly discontinuous shock-induced aerodynamics are discussed.

Erickson, Gary E.↗
DOE OSTI2023

Multi-Objective Adaptive Mesh Refinement Using Reinforcement Learning

Finite element methods approximate the solution to a partial differential equation (PDE) on a mesh consisting of many elements. In general, using more, smaller elements results in a lower error in the approximation. However, it is often possible to lower the error substantially by only refining, or decreasing the size of, the few elements in the mesh that have the highest error. Adaptive mesh refinement (AMR) is a process that selectively refines regions of a mesh with high error to achieve a desired accuracy in as few degrees of freedom (DOFs) as possible. AMR is favorable compared to uniform refinement, which refines all elements of the mesh equally, because it can often achieve the same accuracy without wasting extra computation time on refinement of elements that already have low error. However, it is difficult to know which elements to refine. In this report we explore ways to choose which elements to refine such that we minimize both the resulting error and the cumulative DOFs used in computation. In particular, we introduce a Pareto-front learning algorithm that trains a policy to give the optimal refinement actions to minimize the cumulative DOFs used to achieve a given target error. Such a policy is useful because it can be deployed on many different problem types where different accuracy levels are desired. Furthermore, training a single policy for a range of target errors allows us to use transfer learning to reduce the required training time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Atlas of putative minima and low-lying energy networks of water clusters n = 3 - 25

We report a database consisting of the putative minima and ~3.2 million local minima lying within 5 kcal/mol from the putative minima for water clusters of sizes n = 3 – 25 using an improved version of the Monte Carlo Temperature Basin Paving (MCTBP) global optimization procedure in conjunction with the ab-initio based, flexible, polarizable Thole-Type Model (TTM2.1-F, version 2.1) interaction potential for water. Several of the low-lying structures, as well as lowlying penta-coordinated water networks obtained with the TTM2.1-F potential, were further refined at the Møller-Plesset second order perturbation MP2/aug-cc-pVTZ level of theory. In total, we have identified 3,138,303 networks corresponding to local minima of the clusters n = 3 – 25, whose Cartesian coordinates and relative energies can be obtained from the webpage https://sites.uw.edu/wdbase/. Networks containing penta-coordinated water molecules start to appear at n = 11 and, quite surprisingly, are energetically close (within 1 – 3 kcal/mol) to the putative minima, a fact that has been confirmed from the MP2 calculations. This large database of water cluster minima spanning quite dissimilar hydrogen bonding networks is expected to influence the development and assessment of the accuracy of interaction potentials for water as well as lower scaling electronic structure methods (such as different density functionals). Furthermore, it can also be used in conjunction with data science approaches (including but no limited to neural networks, machine and deep learning) to understand the properties of water, nature’s most important substance.

Rakshit, Avijit↗
DOE OSTI2025

A case study in contrastive learning information combination: Application to technical forensics of additive manufacturing filament source identification

Combination of information from disparate data sources into a single decision is a core challenge in many fields, including the field of technical forensics. Technical forensics (TF) utilizes technical characterization of questioned samples to determine properties of that sample; these properties are then used to infer information of forensic interest, such as provenance, age, or attribution. TF is utilized in traditional forensic applications, such as the attribution of material fragments from an explosive, and in nuclear forensic applications, such as the attribution of actinides which have been interdicted out of regulatory control. The challenge of combining information from disparate sources, described alternately by many terms including “Data Fusion” and “Data Integration”, is exacerbated in the technical forensics domain due to at least two factors: the challenge of interpreting each information source singularly, and the relatively small data set sizes available. Extensive literature exists attempting to combine technical forensics information sources, both in manual and automated processes. These attempts are often bespoke to the specific information sources (such as the bi-, tri-, or quad-isotope chart (Moody, Grant, and Hutcheon 2005)), with some emerging examples of simple early- and late- fusion (, respectively). Simultaneous to the information combination efforts described in the previous paragraph, the field of natural language processing attempted (and largely succeeded) in combining information from multiple non-technical information sources. The ecosystem of “multi-modal” language models, which can take text and images as input, and generate text and images as output, became large and diverse by 2025 (Khan et al. 2025). In a generalized sense, many of these methods are trained by learning neural networks which can convert raw text or images into a vector of numbers describing the text or image, hereafter called “embeddings” and the neural networks performing the conversion are called “embedders”. By using a separate embedder for text and images, finding coincident text and images (such as images with their captions), and optimizing the parameters of the embedders such that the embeddings for the text and the image are similar, the field has found a bridge between text and images (Girdhar et al. 2023). It is the contention of the authors of this report that this insight is not limited to text and images but instead can be extended to any modality which can be found coincidently. The subject of the rest of this report is the application of this method to example multi-modal technical forensic data. Some details about the data used in this report are not appropriate for this report, and are included in a companion report (PNNL-38669).

36 MATERIALS SCIENCE↗
NASA NTRS2001

High Temperature Fatigue Crack Growth Behavior of Alloy 10

Methods to improve the high temperature, dwell crack growth resistance of Alloy 10, a high strength, nickel-base disk alloy, were studied. Two approaches, heat treat variations and composition modifications, were investigated. Under the heat treat approach, solution temperature, cooling rates, and stabilization, were studied. It was found that higher solution temperatures, which promote coarser grain sizes, coupled with a 1550 F stabilization treatment were found to significantly reduce dwell crack growth rates at 1300 F Changes in the niobium and tantalum content were found to have a much smaller impact on crack growth behavior. Lowering the niobium:tantalum ratio did improve crack growth resistance and this effect was most pronounced for coarse grain microstructures. Based on these findings, a coarse grain microstructure for Alloy 10 appears to be the best option for improving dwell crack growth resistance, especially in the rim of a disk where temperatures can reach or exceed 1300 T. Further, the use of advanced processing technologies, which can produce a coarse grain rim and fine grain bore, would be the preferred option for Alloy 10 to obtain the optimal balance between tensile, creep, and crack growth requirements for small gas turbine engines.

Gayda, John↗
DOE OSTI2017

Photocatalytic activity of ZnO nanoparticles with optimization of defects

Graphical abstract: Systematic scheme for photocatalytic activity. Pristine and air quenched ZnO nanoparticles were effectively synthesized by simple combustion method and effectively used as photocatalyst for photodegradtion of methyl orange dye. It was observed that air quenched ZnO nanoparticles shows the better properties as compared to pristine ZnO. The complete degradation of dye was observed in 150 min. - Highlights: • Pristine and air quenched ZnO nanoparticles were synthesized by simple combustion method. • Synthesized ZnO for air quenched ZnO has better optoelectronic properties. • ZnO nanoparticles for air quenched ZnO exhibiting complete photodegradation of Methyl Orange dye under UV irradiation. • Photocatalytic activities for all the samples were observed in 150 min. - Abstract: Tuning of defect levels is the major concern for the application of ZnO nanoparticles. These nanoparticles with different defect concentration were prepared by simple combustion method at 700 °C and subsequent quenching in air. The catalyst was characterized by X-Ray Diffraction (XRD), Field Emission Scanning Electron Microscope (FESEM), UV–vis spectroscopy measurements. XRD reveals that the cell volume increased with large temperature difference between 700 °C and quenched media. FTIR confirmed that the vibrational frequencies shifted from 519 cm{sup −1} to 535 cm{sup −1} point out the role of quenching defects. FESEM shows the size is of the order of 20–45 nm and found irregular distribution of grain boundaries in the quenched induced defects. Different morphology plays a major role in the enhancement of photocatalytic efficiency of synthesized nanoparticles. PL intensity indicates more oxygen vacancies for air quenched ZnO whereas; DTA study demonstrates that the synthesized ZnO is thermally stable. Kinetics study confirmed the photodegradation of methyl orange (MO) dye using this photocatalyst follows first order rate constant. It exhibits the complete degradation of dye only in 150 min under UV irradiation. Kinetic study revealed the rate constant of nanoparticles is 0.0165 min{sup −1}. Air quenched sample showed the higher intensity of defect band as compared to pristine ZnO.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ultra‐Flexible Pixelated Perovskite Photodetectors Enabled by Honeycomb Polymer Grids for High‐Resolution Imaging

Abstract A nature‐inspired fabrication method based on a photolithography‐free flexible polymer grid is reported for high‐resolution pixelation of perovskite photodiode arrays with exceptional mechanical ductility and a morphology resembling that of natural compound eyes. The resulting pixelated perovskite photosensitive layer has a ≈1 µm pixel size with 2000 Pixels per inch (PPI) resolution when fully assembled as a photodetector array, delivering a detectivity of >10 13 Jones while providing cross‐talk free imaging. Using a polymer grid effectively releases stress on the perovskite platform, greatly increasing the mechanical agility of the otherwise brittle perovskite film. This novel fabrication methodology and device design offer new possibilities for applications in robotics, biomedical imaging, and virtual and augmented reality.

Zheng, Ding [Department of Chemistry and the Mater↗
DOE OSTI2024

Nth-plant scenario for blended pellets of Miscanthus, Switchgrass, and Corn Stover using multi-modal transportation: Biorefineries and depots in the contiguous U.S.

The sustainability of the biofuel industry depends on the development of a mature conversion technology on a national level that can take advantage of the economies of scale: the nth-plant. Here, this study addresses the logistic challenge of mobilizing national cellulosic feedstock supplies for a sustainable bioenergy industry. A Mixed Integer Linear Programming (MILP) model was developed and updated to deliver on-spec biomass that considers both a desired quantity and quality at the biorefinery. Our supply chain analysis includes multi-modal transport (truck and rail), varying depot and biorefinery sizes, and feedstock blends of corn stover (harvested by either a two- or three-pass method), switchgrass, and miscanthus. The following US states: Illinois, Kansas, Missouri, North Carolina, Oklahoma, Georgia, and Texas were identified as key locations for producing accessible miscanthus. Based on our most optimistic scenario, using trucks as the only transportation mode in 2040 with a cost target of $\$$79/dt, corn stover, switchgrass, and miscanthus could help meet 48% of the EPA target, 173 million dry tons that translate into 7.8 billion GGE. The addition of rail transportation for biomass delivery to biorefineries could help meet 79% of the EPA target, 283 million dry tons that translate into 12.7 billion GGE.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

A detailed examination of laser-ion acceleration mechanisms in the relativistic transparency regime using tracers

Here we present a particle-in-cell study of linearly polarized laser-ion acceleration systems, in which we use both two-dimensional (2D) and three-dimensional (3D) simulations to characterize the ion acceleration mechanisms in targets which become transparent to the laser pulse during irradiation. First, we perform a target length scan to optimize the peak ion energies in both 2D and 3D, and the predictive capabilities of 2D simulations are discussed. Tracer analysis allows us to isolate the acceleration into stages of target normal sheath acceleration (TNSA), hole boring (HB), and break-out afterburner (BOA) acceleration, which vary in effectiveness based on the simulation parameters. The thinnest targets reveal that enhanced TNSA is responsible for accelerating the most energetic ions, whereas the thickest targets have ions undergoing successive phases of HB and TNSA (in 2D) or BOA and TNSA (in 3D); HB is not observed to be a dominant acceleration mechanism in the 3D simulations. It is in the intermediate optimal regime, both when the laser breaks through the target with appreciable amplitude and when there is enough plasma to form a sustained high density flow, that BOA is most effective and is responsible for the most energetic ions. Eliminating the transverse laser spot size effects by performing a plane wave simulation, we can isolate with greater confidence the underlying physics behind the ion dynamics we observe. Specifically, supplemented by wavelet and FFT analyses, we match the post-transparency BOA acceleration with a wave-particle resonance with a high-amplitude low-frequency electrostatic wave of increasing phase velocity, consistent with that predicted by the Buneman instability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1995

Displacement based multilevel structural optimization

Multidisciplinary design optimization (MDO) is expected to play a major role in the competitive transportation industries of tomorrow, i.e., in the design of aircraft and spacecraft, of high speed trains, boats, and automobiles. All of these vehicles require maximum performance at minimum weight to keep fuel consumption low and conserve resources. Here, MDO can deliver mathematically based design tools to create systems with optimum performance subject to the constraints of disciplines such as structures, aerodynamics, controls, etc. Although some applications of MDO are beginning to surface, the key to a widespread use of this technology lies in the improvement of its efficiency. This aspect is investigated here for the MDO subset of structural optimization, i.e., for the weight minimization of a given structure under size, strength, and displacement constraints. Specifically, finite element based multilevel optimization of structures (here, statically indeterminate trusses and beams for proof of concept) is performed. In the system level optimization, the design variables are the coefficients of assumed displacement functions, and the load unbalance resulting from the solution of the stiffness equations is minimized. Constraints are placed on the deflection amplitudes and the weight of the structure. In the subsystems level optimizations, the weight of each element is minimized under the action of stress constraints, with the cross sectional dimensions as design variables. This approach is expected to prove very efficient, especially for complex structures, since the design task is broken down into a large number of small and efficiently handled subtasks, each with only a small number of variables. This partitioning will also allow for the use of parallel computing, first, by sending the system and subsystems level computations to two different processors, ultimately, by performing all subsystems level optimizations in a massively parallel manner on separate processors. It is expected that the subsystems level optimizations can be further improved through the use of controlled growth, a method which reduces an optimization to a more efficient analysis with only a slight degradation in accuracy. The efficiency of all proposed techniques is being evaluated relative to the performance of the standard single level optimization approach where the complete structure is weight minimized under the action of all given constraints by one processor and to the performance of simultaneous analysis and design which combines analysis and optimization into a single step. It is expected that the present approach can be expanded to include additional structural constraints (buckling, free and forced vibration, etc.) or other disciplines (passive and active controls, aerodynamics, etc.) for true MDO.

Striz, Alfred G.↗
NASA NTRS2016

The Biomolecule Sequencer Project: Nanopore Sequencing as a Dual-Use Tool for Crew Health and Astrobiology Investigations

Human missions to Mars will fundamentally transform how the planet is explored, enabling new scientific discoveries through more sophisticated sample acquisition and processing than can currently be implemented in robotic exploration. The presence of humans also poses new challenges, including ensuring astronaut safety and health and monitoring contamination. Because the capability to transfer materials to Earth will be extremely limited, there is a strong need for in situ diagnostic capabilities. Nucleotide sequencing is a particularly powerful tool because it can be used to: (1) mitigate microbial risks to crew by allowing identification of microbes in water, in air, and on surfaces; (2) identify optimal treatment strategies for infections that arise in crew members; and (3) track how crew members, microbes, and mission-relevant organisms (e.g., farmed plants) respond to conditions on Mars through transcriptomic and genomic changes. Sequencing would also offer benefits for science investigations occurring on the surface of Mars by permitting identification of Earth-derived contamination in samples. If Mars contains indigenous life, and that life is based on nucleic acids or other closely related molecules, sequencing would serve as a critical tool for the characterization of those molecules. Therefore, spaceflight-compatible nucleic acid sequencing would be an important capability for both crew health and astrobiology exploration. Advances in sequencing technology on Earth have been driven largely by needs for higher throughput and read accuracy. Although some reduction in size has been achieved, nearly all commercially available sequencers are not compatible with spaceflight due to size, power, and operational requirements. Exceptions are nanopore-based sequencers that measure changes in current caused by DNA passing through pores; these devices are inherently much smaller and require significantly less power than sequencers using other detection methods. Consequently, nanopore-based sequencers could be made flight-ready with only minimal modifications.

John, K. K.↗