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NASA NTRS1994

Understanding software faults and their role in software reliability modeling

This study is a direct result of an on-going project to model the reliability of a large real-time control avionics system. In previous modeling efforts with this system, hardware reliability models were applied in modeling the reliability behavior of this system. In an attempt to enhance the performance of the adapted reliability models, certain software attributes were introduced in these models to control for differences between programs and also sequential executions of the same program. As the basic nature of the software attributes that affect software reliability become better understood in the modeling process, this information begins to have important implications on the software development process. A significant problem arises when raw attribute measures are to be used in statistical models as predictors, for example, of measures of software quality. This is because many of the metrics are highly correlated. Consider the two attributes: lines of code, LOC, and number of program statements, Stmts. In this case, it is quite obvious that a program with a high value of LOC probably will also have a relatively high value of Stmts. In the case of low level languages, such as assembly language programs, there might be a one-to-one relationship between the statement count and the lines of code. When there is a complete absence of linear relationship among the metrics, they are said to be orthogonal or uncorrelated. Usually the lack of orthogonality is not serious enough to affect a statistical analysis. However, for the purposes of some statistical analysis such as multiple regression, the software metrics are so strongly interrelated that the regression results may be ambiguous and possibly even misleading. Typically, it is difficult to estimate the unique effects of individual software metrics in the regression equation. The estimated values of the coefficients are very sensitive to slight changes in the data and to the addition or deletion of variables in the regression equation. Since most of the existing metrics have common elements and are linear combinations of these common elements, it seems reasonable to investigate the structure of the underlying common factors or components that make up the raw metrics. The technique we have chosen to use to explore this structure is a procedure called principal components analysis. Principal components analysis is a decomposition technique that may be used to detect and analyze collinearity in software metrics. When confronted with a large number of metrics measuring a single construct, it may be desirable to represent the set by some smaller number of variables that convey all, or most, of the information in the original set. Principal components are linear transformations of a set of random variables that summarize the information contained in the variables. The transformations are chosen so that the first component accounts for the maximal amount of variation of the measures of any possible linear transform; the second component accounts for the maximal amount of residual variation; and so on. The principal components are constructed so that they represent transformed scores on dimensions that are orthogonal. Through the use of principal components analysis, it is possible to have a set of highly related software attributes mapped into a small number of uncorrelated attribute domains. This definitively solves the problem of multi-collinearity in subsequent regression analysis. There are many software metrics in the literature, but principal component analysis reveals that there are few distinct sources of variation, i.e. dimensions, in this set of metrics. It would appear perfectly reasonable to characterize the measurable attributes of a program with a simple function of a small number of orthogonal metrics each of which represents a distinct software attribute domain.

Munson, John C.↗

NASA NTRS2006

Volumetric Acoustic Vector Intensity Probe

A new measurement tool capable of imaging the acoustic intensity vector throughout a large volume is discussed. This tool consists of an array of fifty microphones that form a spherical surface of radius 0.2m. A simultaneous measurement of the pressure field across all the microphones provides time-domain near-field holograms. Near-field acoustical holography is used to convert the measured pressure into a volumetric vector intensity field as a function of frequency on a grid of points ranging from the center of the spherical surface to a radius of 0.4m. The volumetric intensity is displayed on three-dimensional plots that are used to locate noise sources outside the volume. There is no restriction on the type of noise source that can be studied. The sphere is mobile and can be moved from location to location to hunt for unidentified noise sources. An experiment inside a Boeing 757 aircraft in flight successfully tested the ability of the array to locate low-noise-excited sources on the fuselage. Reference transducers located on suspected noise source locations can also be used to increase the ability of this device to separate and identify multiple noise sources at a given frequency by using the theory of partial field decomposition. The frequency range of operation is 0 to 1400Hz. This device is ideal for the study of noise sources in commercial and military transportation vehicles in air, on land and underwater.

Klos, Jacob↗

NASA NTRSDate not supplied

Medical System Foundation Overview for Long-Duration Lunar Orbit and Surface Operations Missions

The Human Research Program (HRP) Exploration Medical Capability (ExMC) Element has developed a Medical System Foundation for Level of Care IV, as defined by NASA’s space flight human-system standards, for long-duration lunar orbit and surface operation missions by employing a systems engineering approach using model-based systems engineering tools. This Foundation model includes a concept of operations; functional decomposition; clinical content (medical conditions, capabilities, and resources); associated functional, interface and non-functional technical requirements; and traces to the current versions of NASA standards documents and parent-level (Program- and Vehicle habitat system level) requirements. Collectively, these components constitute a foundation that serves as a starting point for a medical system that meets the Level of Care IV requirement. The Foundation was developed by a multidisciplinary team consisting of systems engineers, scientists, and clinicians across NASA, and information is presented in an easily accessible format that is understandable across disciplines. Stakeholders can use the Foundation to analyze the traces between medical capabilities, medical conditions, medical resources, and requirements and to identify medical system interfaces with other vehicle systems/subsystems. It can also be used as a basis for performing trades on risks vs. medical system mass and volume allocation. This discussion will focus on the processes through which the Medical System Foundation was developed, how the Foundation builds a bridge between the medical and engineering domains and facilitates communication between these communities, and how these processes can be applied more broadly to a crew health and performance system and other system domains. The presentation also discusses Foundation modifications based on the recently updated versions of the NASA 3001 Standards.

S. Lumpkins↗

DOE OSTI2025

Bacterial and fungal growth on fungal necromass and its diverse components: Shared profiles and divergent constraints revealed by high‐throughput phenotyping

1. While fungal necromass is increasingly recognized as a major source of persistent carbon (C) in soils, the relative functional roles of bacteria and fungi in decomposing necromass are not fully resolved, and the processes that select for necromass decomposer communities from the broader soil microbial community are an emerging area of interest. 2. In this study, we characterized the growth of 52 bacterial and 83 fungal strains isolated from necromass and soil on 22 C substrates, including different necromass phenotypes, fungal cell wall polymers, dimers and monomers. 3. We found that the isolation habitat of the strains used in this experiment (necromass vs. soil) had no effect on the substrates they were able to use. Isolates from both microbial domains were able to grow on different labile carbon substrates, polymers and necromass phenotypes. However, fungal growth was most limited by necromass melanin content, while bacterial growth was more limited by the abundance of cell wall polysaccharides. Additionally, overall differences in substrate use between bacteria and fungi were most pronounced on polymer substrates. 4. Collectively, our results suggest that there is substantial functional overlap in necromass substrate use across microbial domains, but some notable differences in bacterial and fungal utilization of cell wall polymers, which can function as a direct energy source or a means of accessing other compounds within necromass. Future studies assessing bacteria and fungi decomposing necromass together rather than in isolation will help to uncover potential physical and chemical interactions within and between these two domains during the decay of this important source of persistent soil C.

dead fungal biomass↗

DOE OSTI2025

Fully Homomorphic Encryption

This code implements a Fully Homomorphic Encryption (FHE) system, enabling secure computation on encrypted data without requiring decryption. It supports encryption, decryption, and homomorphic operations like matrix multiplication and addition. This code is adaptable for integrating FHE into linear-time invariant (LTI) systems, including digital control and filtering. With proper configuration from subject matter expertise, encrypted system parameters and signals can be manipulated to perform tasks like state updates, output calculations, and convolution in the encrypted domain. By preserving the structure of LTI systems while ensuring privacy, the framework facilitates secure applications in areas such as autonomous systems, signal processing, and industrial automation. The code initializes the encryption system using parameters provided in the env dictionary. These parameters include the ciphertext modulus, key dimension, plaintext fixed-point scaling factor, and noise bound. During initialization, a secret key is generated, which is essential for encrypting and decrypting data securely. The modular design allows users to tailor these parameters to specific use cases or security requirements. The code implements multiple cryptographic schemes. The learning with errors (LWE) encryption method encodes cleartext message to their plaintext fixed-point representation then encrypted into ciphertext space with additive noise. This noise ensures the security of the scheme, relying on the computational hardness of the LWE problem. The code also includes the Gentry-Sahai-Waters (GSW) scheme based off the LWE problem. Homomorphic matrix multiplication is performed between the LWE and GSW to encrypted data. This is achieved using a decomposition function on the LWE ciphertext during the multiplication operation. For higher-dimensional data, the code includes a method to encrypt entire matrices (GSWMat) using GSW encryption. These encrypted matrices can then be used for homomorphic matrix multiplications (MatMult). The decryption function uses the secret key to recover the original plaintext, removing the added noise and scaling that was originally applied during encryption.

Lois, Roberts [Idaho National Laboratory (INL), Id↗

NASA NTRS2008

Fabricating Large-Area Sheets of Single-Layer Graphene by CVD

This innovation consists of a set of methodologies for preparing large area (greater than 1 cm(exp 2)) domains of single-atomic-layer graphite, also called graphene, in single (two-dimensional) crystal form. To fabricate a single graphene layer using chemical vapor deposition (CVD), the process begins with an atomically flat surface of an appropriate substrate and an appropriate precursor molecule containing carbon atoms attached to substituent atoms or groups. These molecules will be brought into contact with the substrate surface by being flowed over, or sprayed onto, the substrate, under CVD conditions of low pressure and elevated temperature. Upon contact with the surface, the precursor molecules will decompose. The substituent groups detach from the carbon atoms and form gas-phase species, leaving the unfunctionalized carbon atoms attached to the substrate surface. These carbon atoms will diffuse upon this surface and encounter and bond to other carbon atoms. If conditions are chosen carefully, the surface carbon atoms will arrange to form the lowest energy single-layer structure available, which is the graphene lattice that is sought. Another method for creating the graphene lattice includes metal-catalyzed CVD, in which the decomposition of the precursor molecules is initiated by the catalytic action of a catalytic metal upon the substrate surface. Another type of metal-catalyzed CVD has the entire substrate composed of catalytic metal, or other material, either as a bulk crystal or as a think layer of catalyst deposited upon another surface. In this case, the precursor molecules decompose directly upon contact with the substrate, releasing their atoms and forming the graphene sheet. Atomic layer deposition (ALD) can also be used. In this method, a substrate surface at low temperature is covered with exactly one monolayer of precursor molecules (which may be of more than one type). This is heated up so that the precursor molecules decompose and form one monolayer of the target material.

Bronikowski, Michael↗

NASA NTRSDate not supplied

Quantifying Emergent Fluid Dynamics Using Reynolds-Interpolated Fluid Reduced-order Models

Fluid reduced-order models (ROMs) which capture the flow physics within the problem's physical domain are usually constrained in accuracy to only the parameter points, e.g. Reynolds and Mach numbers, at which reference data was provided. Interpolation-focused quantity-of-interest ROMs are often structured differently and fail to provide flow volume data with the same quality - if at all. In this paper, techniques which reside at the intersection of these two ROM schools - flow physics ROMs which can be interpolated within a parameter space of interest - are explored. Using a combination of existing and novel techniques, emergent physics are identified using a fluid ROM at parameter points which are not provided in the ROM's training data.

uncertainty quantification↗

NASA NTRSDate not supplied

Quantifying Emergent Fluid Dynamics Using Reynolds-Interpolated Fluid Reduced-order Models

uncertainty quantification↗

NASA NTRSDate not supplied

Problem Complexity and LLM: H-M Team Reliability in Challenging Environments

In traditional human-machine operations, the functional decomposition of actions and responsibilities among various agents is assigned a priori. For instance, in current air traffic operations, although assisted by software, human pilots have the ultimate control of aircraft. Multiagent human-machine and machine-machine systems will face problems of varying and potentially unpredicted complexity in future challenging scenarios of planetary, en route, and orbital activities. Hence, it is important to enable dynamic transfer of decision-making to appropriate team members, human or machine, depending on which agent is best equipped to solve that specific problem on a time budget. In this paper, we consider aspects of problem-solving and its modeling that affect the outcomes of decision-making as a function of solution quality and the likelihood of solving the problem on a required time budget. We focus on Large Language Models (LLM) as potential machine teammates and conclude that practical, predictive modeling of their performance, at the current stage of their development, is infeasible. Simple examples help us illustrate that current LLM will require fundamental advancements to provide reliable support in team decision-making, especially in safety-critical and time-critical domains. The study is not meant to diminish the value of the remarkable capabilities of LLM, but rather to gain a better understanding of the technology’s appropriate use and the needed additions.

function allocation↗

DOE OSTI2026

Primordial black hole dark matter: A quantitative parameter sensitivity comparison across formation mechanisms and particle candidates

Primordial black holes (PBHs) in the asteroid-mass window ( 10 17 – 10 22 g ) can account for all of the dark matter without violating any observational constraint, yet are routinely dismissed as fine-tuned. I put that dismissal to the test by applying three complementary sensitivity measures uniformly across a broad landscape: three noninflationary PBH production mechanisms, six classes of inflationary PBH models, and seven particle dark matter benchmarks, all evaluated against the same observable target. Three distinct naturalness universality classes emerge, determined entirely by the analytic structure of the abundance map rather than by the nature of the dark matter candidate. Biased-domain-wall PBHs, in their least model-dependent (free- V b ) form, have the same low sensitivity, Δ = 4.5 , as off-resonance weakly interacting massive particles and freeze-in particles ( Δ = 2 ), a sensitivity that, because it is constant over the entire parameter space of the construction, also coincides trivially with its own Wilson-normalized average within that parameter space (Section Definition and conventions), an equivalence that concerns only the space over which Δ is computed and is not a naturalness statement about the construction as a whole; a further reduction to Δ = 2 is possible only under the additional, independently motivated but not required, assumption that the domain-wall bias is generated by Planck-suppressed operators; early matter-domination PBHs occupy an intermediate tier alongside coannihilating weakly interacting massive particles (WIMPs), unified by a structural identity in which the sensitivity measure equals the logarithm of the ratio of the formation scale to the matter–radiation equality scale; first-order phase transition PBHs, once the more accurate super-exponential collapse probability is used in place of the single-exponential approximation, instead belong to the same highly sensitive tier as resonant WIMP annihilation and single-field inflationary collapse, for a structurally distinct reason; single-field ultraslow-roll inflationary collapse is severely tuned for a distinct reason: a double exponential in which the power spectrum amplitude is itself exponentially sensitive to the inflaton potential coefficients, on top of the exponential collapse sensitivity of the abundance map. My main conclusion is that the claim that PBH dark matter is generically fine-tuned conflates the worst case with a landscape spanning every naturalness tier. The Barbieri-Giudice sensitivity computed here and the Wilson-normalized measure of Iovino and Riotto answer distinct and mutually consistent questions about the same construction, a distinction I clarify within the two-layer decomposition.

Profumo, Stefano [University of California, Santa ↗

NASA NTRS1998

Data Mining and Optimization Tools for Developing Engine Parameters Tools

This project was awarded for understanding the problem and developing a plan for Data Mining tools for use in designing and implementing an Engine Condition Monitoring System. From the total budget of $5,000, Tricia and I studied the problem domain for developing ail Engine Condition Monitoring system using the sparse and non-standardized datasets to be available through a consortium at NASA Lewis Research Center. We visited NASA three times to discuss additional issues related to dataset which was not made available to us. We discussed and developed a general framework of data mining and optimization tools to extract useful information from sparse and non-standard datasets. These discussions lead to the training of Tricia Erhardt to develop Genetic Algorithm based search programs which were written in C++ and used to demonstrate the capability of GA algorithm in searching an optimal solution in noisy datasets. From the study and discussion with NASA LERC personnel, we then prepared a proposal, which is being submitted to NASA for future work for the development of data mining algorithms for engine conditional monitoring. The proposed set of algorithm uses wavelet processing for creating multi-resolution pyramid of the data for GA based multi-resolution optimal search. Wavelet processing is proposed to create a coarse resolution representation of data providing two advantages in GA based search: 1. We will have less data to begin with to make search sub-spaces. 2. It will have robustness against the noise because at every level of wavelet based decomposition, we will be decomposing the signal into low pass and high pass filters.

Dhawan, Atam P.↗

NASA NTRSDate not supplied

Long Duration Medical System Foundation for Lunar Orbital and Lunar Surface Exploration Missions

For long-duration lunar orbital and lunar surface (LDLOS) exploration missions, the NASA Human Research Program (HRP) Exploration Medical Capability (ExMC) Element has developed a medical system foundation through which clinical considerations may be represented via a systems engineering-based model. Components of the Long Duration Medical System Foundation model include a concept of operations (ConOps), functional decomposition, medical conditions to be addressed, clinical capabilities and resources, technical requirements, and traces of requirements to NASA standards documents and parent-level (Program- and Vehicle habitat system-level) requirements. The Foundation model offers the means to present information in a readily accessible format that is understandable across all clinical, engineering, scientific, and managerial disciplines. Collectively, these components constitute a foundation that serves future programs with similar long duration mission profiles as a starting point for medical system design. The Foundation was developed by a multidisciplinary team of systems engineers, scientists, and clinicians across NASA. The process started with ConOps development, subsequently decomposed into the functionalities needed to diagnose, treat, and prevent medical conditions. The clinical team identified medical conditions most likely needed to be diagnosed and treated during a long-duration lunar exploration mission. Through these approaches, requirements were codified for the LDLOS medical system. These requirements were then traced to NASA standards, medical conditions, medical capabilities, and medical resources, facilitating stakeholders’ use of the Foundation model to analyze traces and to identify medical system interfaces with other vehicle systems or subsystems. In addition, the Foundation may be used as a basis for performing risk trades on medical system mass and volume allocation. This discussion will focus on the processes through which the LDLOS Medical System Foundation was developed, how the Foundation builds a bridge between the medical and engineering domains, and how these processes may be applied more broadly to a crew health and performance system and other system domains.

Jay Lemery↗

DOE OSTI2025

Unraveling the Atomic Mechanism of the Crystalline Phase‐Dependent Structural Features and Special Spectral Design of α‐, β‐, and Ɛ‐Ga₂O₃

Atomic‐scale phase transformations profoundly influence the functional properties of Ga₂O₃ polymorphs. By combining irradiation experiments with microstructure characterization and theoretical approaches, phase‐specific energy‐dissipation pathways in α‐, β‐, and ε‐Ga₂O₃ are uncovered and strategies for targeted property design are outlined. Competing antiphase boundaries (APBs) and twin domain boundaries (TDBs) promote irreversible α→ε interconversion through domain fragmentation. In β‐Ga₂O₃, defect‐induced stress gradients drive two distinct local transformations: surface Ga‐aggregated β→δ that stabilizes transient states, and latent‐track‐confined β→κ phase transition with recoverable distortions via cation reordering. Under electronic excitation, β‐Ga₂O₃ forms nanohillocks via robust GaO₆ octahedra (high density/strong Ga─O bonds), while α/ε‐Ga₂O₃ generates nanopores from tetrahedral Ga looseness (low bonding energy), highlighting phase‐dependent surface dynamics shaped by atomic packing and bonding anisotropy. Defect‐regulated recombination suppresses visible photoluminescence in α/β‐Ga₂O₃, whereas in ε‐Ga₂O₃ bandgap narrowing of ΔE: 0.30 eV is observed, enhancing emission. Linking phase‐dependent defect‐carrier interactions and metastable‐phase engineering in Ga₂O₃ enables property optimization for power‐electronics and optoelectronics devices.

electronic state configuration↗

DOE OSTI2025

Aerodynamic Sensitivities over Separable Shape Tensors

Here, we present a comprehensive aerodynamic sensitivity analysis of airfoil parameterization informed by separable shape tensors. This parameterization approach uniquely benefits the design process by isolating various well-studied shape characteristics, such as airfoil thickness, and providing a well-regulated low-dimensional parameter domain for aerodynamic designs. Exploring the aerodynamic sensitivities of this novel parameterization can provide valuable insights for more robust designs and future manufacturing efforts. We construct a data-driven parameter space of airfoils using principal geodesic analysis of separable shape tensors informed by a curated database containing almost 20,000 suitable engineering airfoils. Analyzing the shape reconstruction error and the maximum mean discrepancy between joint distributions of aerodynamic quantities, we study the dimensionality of the learned parameter space. This simple numerical experiment demonstrates a dramatic dimension reduction that retains design effectiveness and promotes regularity of the shape representations. Finally, we generate new airfoils and use the HAM2D Reynolds-averaged Navier–Stokes solver to predict lift, drag, and moment coefficients. We compute multiple sensitivity metrics to quantify and assert the consistency of parameter influence on the aerodynamic quantities. We also explore low-dimensional polynomial ridge approximations to motivate physical intuitions and offer explanations of the approximated sensitivities.

17 WIND ENERGY↗

DOE OSTI2025

Physics-guided dual implicit neural representations for source separation

Significant challenges exist in efficient data analysis of most advanced experimental and observational techniques because the collected signals often include unwanted contributions, such as background and signal distortions, that can obscure the physically relevant information of interest. To address this, we have developed a self-supervised machine-learning approach for source separation using a dual implicit neural representation framework that jointly trains two neural networks: one for approximating distortions of the physical signal of interest and the other for learning the effective background contribution. Our method learns directly from the raw data by minimizing a reconstruction-based loss function without requiring labeled data or pre-defined dictionaries. We demonstrate the effectiveness of our framework by considering a challenging case study involving large-scale simulated, as well as experimental, momentum-energy-dependent inelastic neutron scattering data in a four-dimensional parameter space, characterized by heterogeneous background contributions and unknown distortions to the target signal. The method is found to successfully separate physically meaningful signals from a complex or structured background even when the signal characteristics vary across all four dimensions of the parameter space. An analytical approach that informs the choice of the regularization parameter is presented. Our method offers a versatile framework for addressing source separation problems across diverse domains, ranging from superimposed signals in astronomical measurements to structural features in biomedical image reconstructions.

47 OTHER INSTRUMENTATION↗