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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Plant synthetic biology as a tool to help eliminate hidden hunger

Agricultural systems are under increasing pressure from declining environmental conditions, a growing population, and changes in consumer preferences, resulting in widespread malnutrition-related illnesses. Improving plant nutritional content through biotechnology techniques such as synthetic biology is a promising strategy to help combat hidden hunger caused by the lack of affordable and healthy foods in human diets. Production of compounds usually found in animal-rich diets, such as vitamin D or omega-3 fatty acids, has been recently demonstrated in planta. Here, we review recent biotechnological approaches to biofortifying plants with vitamins, minerals, and other metabolites, and summarise synthetic biology advances that offer the opportunity to build on these early biofortification efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

An iterative learning and inference approach to managing dynamic cyber-vulnerabilities of complex systems

As modern systems become increasingly reliant on cyber-technologies and continue to be integrated with physical systems, managing risks from deliberate and non-deliberate sources is a significant research challenge. Unlike strictly physical systems, cyber-enabled physical systems are influenced by dynamic and evolving technologies, environments, and attack mechanisms. As a result, vulnerabilities are rapidly changing and difficult to detect and manage. While there is recent interest in the dynamic properties of performance through resilience analysis, limited research addresses the dynamic nature of cyber-system vulnerability. This paper presents a dynamic iterative learning approach to evaluate the state of health for systems as related to cyber-vulnerabilities. These time-varying system health characteristics may not be visible, but can be inferred using observable characteristics. The approach recognizes that multiple types of vulnerabilities need to be included in a holistic system health assessment. The methods are applied to the Common Vulnerability Scoring System database containing over 100,000 documented cybersecurity vulnerabilities. This work is the first to acknowledge the dynamic properties of cyber-vulnerability, while also inferring system health using observable data and hidden states of system health. This work will be of interest to managers of large-scale cyber-enabled physical systems who are seeking to prioritize system health investments.

Chatterjee, Samrat↗
DOE OSTI2023

Geothermal Play Fairway Analysis Best Practices

Play fairway analysis (PFA) is a methodology that can improve success rates for geothermal exploration drilling, thus reducing the costs of geothermal projects while facilitating development in new areas. It was originally developed for the oil and gas industry, but has been adapted for discovering geothermal resources over the last decade. The geothermal PFA methodology involves systematically screening a set geographic area for promising qualities typically related to the presence of heat, permeability, and fluid. Successful application of PFA can identify hidden hydrothermal systems. From 2014 to 2021 the U.S. Department of Energy (DOE) Geothermal Technologies Office (GTO) supported the development of PFA for geothermal resources through awards to 11 research teams across the country. The goal of these projects was to advance and adapt PFA for geothermal exploration to produce regional-scale maps that reduce exploration uncertainty. This report is an outcome of the NREL-led PFA Retrospective project, which compiled, synthesized, analyzed the results of GTO's geothermal PFA program. Ultimately, we find that these projects greatly advanced approaches to geothermal exploration and resulted in extensive new data and new discoveries of unrecognized geothermal systems. We used the results to distill best practices in this report and to provide guidance for future applications of geothermal PFA.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Detecting and Characterizing Fracture Zones Using a Convolutional Neural Network

This project directly supports the Geothermal Technologies Office (GTO) objectives outlined in the Multi-Year Program Plan (MYPP) by advancing two key research areas: “Exploration and Characterization” and “Data, Modeling, and Analysis.” This project has successfully demonstrated a pre-drilling ability to image and characterize the distribution and connectivity of subsurface faults and fractures, key parameters for identifying permeable pathways that enable geothermal fluids to circulate and produce energy. Specifically, we developed and implemented innovative machine learning methodologies to enhance geothermal exploration. Large-scale faults were detected using a Convolutional Neural Network (CNN), while small-scale fractures were characterized using a novel Double-Beam Neural Network (DBNN). These tools have proven both technically effective and cost-efficient by reducing reliance on expensive exploratory drilling. Through collaboration with our geothermal industry partner, this research has significantly advanced techniques for identifying hidden geothermal systems and extending the productive lifespan of existing geothermal fields. We applied our methods to two geothermal fields—Soda Lake (Nevada) and Lightning Dock (New Mexico)—to identify shallow steam-charged fracture zones and characterize deep faults at depths of 1.5-2 km. The steam zone identified at the Soda Lake geothermal field showed excellent agreement with prior drilling data, validating the effectiveness of our approaches. In addition, the analysis revealed three new prospective drilling targets for further development and verification. The outcomes of this project improve our scientific understanding of geothermal reservoir behavior, enhance exploration efficiency, extend the economic life of existing geothermal plants. Ultimately, these advancements contribute to GTO’s goal of achieving more sustainable, affordable, and data-driven geothermal energy development across the United States.

15 GEOTHERMAL ENERGY↗
NASA NTRS2009

T/R Multi-Chip MMIC Modules for 150 GHz

Modules containing multiple monolithic microwave integrated-circuit (MMIC) chips have been built as prototypes of transmitting/receiving (T/R) modules for millimeter-wavelength radar systems, including phased-array radar systems to be used for diverse purposes that could include guidance and avoidance of hazards for landing spacecraft, imaging systems for detecting hidden weapons, and hazard-avoidance systems for automobiles. Whereas prior landing radar systems have operated at frequencies around 35 GHz, the integrated circuits in this module operate in a frequency band centered at about 150 GHz. The higher frequency (and, hence, shorter wavelength), is expected to make it possible to obtain finer spatial resolution while also using smaller antennas and thereby reducing the sizes and masses of the affected systems.

Samoska, Lorene A.↗
DOE OSTI2021

Sequential Bayesian inference of transition rates in the hidden Markov model for multi-state system degradation

The more easily available system performance data and advances in data analytics have provided us with opportunities to optimize maintenance programs for engineered systems, for example nuclear power plants. One key task in maintenance optimization is to obtain an accurate model for system degradation. Here, in this research, we propose a Bayesian method to address this problem. Noting that systems usually exhibit multiple states and that the actual state of a system usually is not directly observable, in the method we first model the system degradation process and the observation process based on a hidden Markov model. Then we develop a sequential Bayesian inference algorithm based on importance sampling and the forward algorithm to infer the posterior distributions of the transition rates in the hidden Markov model based on available observations. The proposed Bayesian method allows us to take advantage of evidence from multiple sources, and also allows us to perform Bayesian inference sequentially, without the need to use the entire history of observations every time new observations are collected. We demonstrate the proposed method using both synthetic data for a nuclear power plant feedwater pump and realistic data for a nuclear power plant chemistry analytical device.

42 ENGINEERING↗
NASA NTRS1994

Hidden Markov Models for Fault Detection in Dynamic Systems

Continuous monitoring of complex dynamic systems is an increasingly important issue in diverse areas such as nuclear plant safety, production line reliability, and medical health monitoring systems. Recent advances in both sensor technology and computational capabilities have made on-line permanent monitoring much more feasible than it was in the past. In this paper it is shown that a pattern recognition system combined with a finite-state hidden Markov model provides a particularly useful method for modelling temporal context in continuous monitoring. The parameters of the Markov model are derived from gross failure statistics such as the mean time between failures. The model is validated on a real-world fault diagnosis problem and it is shown that Markov modelling in this context offers significant practical benefits.

Smyth, Padhraic↗
NASA NTRS2007

Experimental Design for the Evaluation of Detection Techniques of Hidden Corrosion Beneath the Thermal Protective System of the Space Shuttle Orbiter

The detection of corrosion beneath Space Shuttle Orbiter thermal protective system is traditionally accomplished by removing the Reusable Surface Insulation tiles and performing a visual inspection of the aluminum substrate and corrosion protection system. This process is time consuming and has the potential to damage high cost tiles. To evaluate non-intrusive NDE methods, a Proof of Concept (PoC) experiment was designed and test panels were manufactured. The objective of the test plan was three-fold: establish the ability to detect corrosion hidden from view by tiles; determine the key factor affecting detectability; roughly quantify the detection threshold. The plan consisted of artificially inducing dimensionally controlled corrosion spots in two panels and rebonding tile over the spots to model the thermal protective system of the orbiter. The corrosion spot diameter ranged from 0.100" to 0.600" inches and the depth ranged from 0.003" to 0.020". One panel consisted of a complete factorial array of corrosion spots with and without tile coverage. The second panel consisted of randomized factorial points replicated and hidden by tile. Conventional methods such as ultrasonics, infrared, eddy current and microwave methods have shortcomings. Ultrasonics and IR cannot sufficiently penetrate the tiles, while eddy current and microwaves have inadequate resolution. As such, the panels were interrogated using Backscatter Radiography and Terahertz Imaging. The terahertz system successfully detected artificially induced corrosion spots under orbiter tile and functional testing is in-work in preparation for implementation.

Kemmerer, Catherine C.↗
DOE OSTI2021

Twisted symmetric trilayer graphene: Single-particle and many-body Hamiltonians and hidden nonlocal symmetries of trilayer moiré systems with and without displacement field

Here, we derive the Hamiltonian for trilayer moiré systems with the Coulomb interaction projected onto the bands near the charge neutrality point. Motivated by the latest experimental results, we focus on the twisted symmetric trilayer graphene (TSTG) with a mirror symmetry with respect to the middle layer. We provide a full symmetry analysis of the noninteracting Hamiltonian with a perpendicular displacement field coupling the band structure made otherwise of the twisted bilayer graphene (TBG) and the high-velocity Dirac fermions, and we identify a hidden nonlocal symmetry of the problem. In the presence of this displacement field, we construct an approximate single-particle model, akin to the tripod model for TBG, capturing the essence of noninteracting TSTG. We also derive more quantitative perturbation schemes for the low-energy physics of TSTG with displacement field, obtaining the corresponding eigenstates. This allows us to obtain the Coulomb interaction Hamiltonian projected in the active band TSTG wave functions and derive the full many-body Hamiltonian of the system. We also provide an efficient parametrization of the interacting Hamiltonian. Finally, we show that the discrete symmetries at the single-particle level promote the U (2) × U (2) spin-valley symmetry to enlarged symmetry groups of the interacting problem under different limits. The interacting part of the Hamiltonian exhibits a large U (4) × U (4) × U (4) × U (4) symmetry in the chiral limit. Moreover, by identifying a symmetry which we dub spatial many-body charge conjugation, we show that the physics of TSTG is symmetric around charge neutrality.

2-dimensional systems↗
DOE OSTI2025

Ferroaxial density wave from intertwined charge and orbital order in rare-earth tritellurides

The discovery of the axial amplitude mode—commonly referred to as the Higgs mode—in charge density wave systems, such as rare-earth tritellurides, indicates the presence of a hidden order. A theoretical study proposed that this axial Higgs mode arises from a hidden orbital texture of the charge density wave, which produces a ferroaxial charge order. However, experimental evidence for the specific hidden order has been lacking. Here, we demonstrate a ferroaxial order of electronic origin throughout the rare-earth tritellurides. In ErTe 3 and HoTe 3 , which exhibit two distinct charge density waves with different ordering temperatures, a detailed investigation shows that the high-temperature charge order phase breaks translational, rotational and all vertical as well as diagonal mirror symmetries. Furthermore, this phase produces an axial Higgs mode and an axial electronic gap. By contrast, the low-temperature phase breaks only translational symmetry and gives rise to a scalar Higgs mode. Notably, both phases preserve the space inversion and time-reversal symmetries. These findings are consistent with a ferroaxial phase driven by coupled orbital and charge orders, highlighting the role of Higgs modes in revealing hidden orders in systems with intertwined charge density waves.

36 MATERIALS SCIENCE↗
NASA NTRS2007

Experimental Design for the Evaluation of Detection Techniques of Hidden Corrosion Beneath the Thermal Protective System of the Space Shuttle Orbiter

The United States Space Operational Space Shuttle Fleet Consists of three shuttles with an average age of 19.7 years. Shuttles are exposed to corrosive conditions while undergoing final closeout for missions at the launch pad and extreme conditions during ascent, orbit, and descent that may accelerate the corrosion process. Structural corrosion under TPS could progress undetected (without tile removal) and eventually result in reduction in structural capability sufficient to create negative margins of . safety and ultimate loss of local structural capability.

Kammerer, Catherine C.↗
DOE OSTI2024

Machine-assisted discovery of integrable symplectic mappings

Integrable systems possess a hidden symmetry associated with the existence of conserved quantities known as integrals of motion. These systems play an important role in understanding general dynamics in accelerators and have potential for future designs. This work will cover two automated methods for finding integrable symplectic maps of the plane. The first algorithm is based on the observation that the evolution of an integrable system in phase space is confined to a lower-dimensional submanifold of a specific type. The second algorithm relies on an analysis of dynamical variables. Both methods rediscover some of the famous McMillan-Suris integrable mappings and ultra-discrete Painlev\'e equations. Over 100 new integrable families are presented and analyzed, some of which are isolated in the space of parameters, while others are families with one parameter (or the ratio of parameters) being either continuous or discrete. In addition, the newly discovered maps are related to a general 2D symplectic map through the use of discrete perturbation theory. A method is proposed for constructing smooth near-integrable dynamical systems based on mappings with polygon invariants.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Machine-Assisted Discovery of Integrable Symplectic Mappings

Integrable systems possess a hidden symmetry associated with the existence of conserved quantities known as integrals of motion. These systems play an important role in understanding general dynamics in accelerators and have potential for future designs. This work will cover two automated methods for finding integrable symplectic maps of the plane. The first algorithm is based on the observation that the evolution of an integrable system in phase space is confined to a lower-dimensional submanifold of a specific type. The second algorithm relies on an analysis of dynamical variables. Both methods rediscover some of the famous McMillan-Suris integrable mappings and ultra-discrete Painlev\'e equations. Over 100 new integrable families are presented and analyzed, some of which are isolated in the space of parameters, while others are families with one parameter (or the ratio of parameters) being either continuous or discrete. In addition, the newly discovered maps are related to a general 2D symplectic map through the use of discrete perturbation theory. A method is proposed for constructing smooth near-integrable dynamical systems based on mappings with polygon invariants.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Machine Learning–Guided Boolean Matrix Inference for Real-Time O-RAN Conflict Detection

Open Radio Access Networks (O-RAN) are emerging, software-driven cellular architectures that promote flexibility by enabling components from different vendors to interoperate. Multiple control applications called xApps can independently adjust network parameters in near real time, often without awareness of each other's actions. This creates a system highly prone to unintended conflicts and performance degradation due to the inherent complexity of such openness. To model such systems and ultimately prevent or mitigate xApp conflicts, it is essential to understand the dynamic relationships between xApps (A), the control parameters they adjust (P), and the resulting KPI responses (K). While the mappings from A to P and from K to A can often be derived from xApp specifications, the relationship from P to K is typically hidden within the system’s dynamics and must be inferred from observed data. We propose a novel data-driven Boolean inference framework that uncovers the hidden P?K dependencies using machine learning and interpretable rule induction. Continuous parameters and KPIs are first binarized using decision tree classifiers, and a binary influence matrix L is then inferred by solving Boolean matrix equations over time. This compact representation improves interpretability and enables real-time tracking of dynamically evolving parameter-KPI dependencies. We demonstrate the effectiveness of our method in a realistic mobile handover scenario, where it accurately recovers the underlying logic and enables proactive conflict detection.

42 - ENGINEERING↗
NASA NTRS1994

Improved Hidden-Markov-Model Method Of Detecting Faults

Method of automated, continuous monitoring to detect faults in complicated dynamic system based on hidden-Markov-model (HMM) approach. Simpler than another, recently proposed HMM method, but retains advantages of that method, including low susceptibility to false alarms, no need for mathematical model of dynamics of system under normal or faulty conditions, and ability to detect subtle changes in characteristics of monitored signals. Examples of systems monitored by use of this method include motors, turbines, and pumps critical in their applications; chemical-processing plants; powerplants; and biomedical systems.

Smyth, Padhraic J.↗
NASA NTRS2006

Eta Carinae: Orientation of The Orbital Plane

Evidence continues to build that Eta Carinae is a massive binary system with a hidden hot companion in a highly elliptical orbit. We present imaging and spectroscopic evidence that provide clues to the orientation of the orbital plane. The circumstellar ejecta, known as the Homunculus and Little Homunculus, are hourglass-shaped structures, one encapsulated within the other, tilted at about 45 degrees from the sky plane. A disk region lies between the bipolar lobes. Based upon their velocities and proper motions, Weigelt blobs B, C and D, very bright emission clumps 0.1 to 0.3" Northwest from Eta Carinae, lie in the disk. UV flux from the hot companion, Eta Car B, photoexcites the Weigelt blobs. Other clumps form a complete chain around the star, but are not significantly photoexcited. The strontium filament, a 'neutral' emission structure, lies in the same general direction as the Weigelt blobs and exhibits peculiar properties indicative that much mid-UV, but no hydrogen-ionizing radiation impinges on this structure. It is shielded by singly-ionized iron. P Cygni absorptions in Fe I I lines, seen directly in line of sight from Eta Carinae, are absent in the stellar light scattered by the Weigelt blobs. Rather than a strong absorption extending to -600 km/s, a low velocity absorption feature extends from -40 to -150 km/s. No absorbing Fe II exists between Eta Carinae and Weigelt D, but the outer reaches of the wind are intercepted in line of sight from Weigelt D to the observer. This indicates that the UV radiation is constrained by the dominating wind of Eta Car A to a small cavity carved out by the weaker wind of Eta Car B. Since the high excitation nebular lines are seen in the Weigelt blobs at most phases, the cavity, and hence the major axis of the highly elliptical orbit, must lie in the general direction of the Weigelt blobs. The evidence is compelling that the orbital major axis of Eta Carinae is projected at -45 degrees position angle on the sky. Moreover the milliarcsecond-scale extended structure of Eta Carinae, recently detected by VLTI, may be evidence of the binary companion in the disk plane, not necessarily of a single star as a prolate spheroid extending along the ejecta polar axis.

Gull, T. R.↗
NASA NTRS2009

Low-Noise MMIC Amplifiers for 120 to 180 GHz

Three-stage monolithic millimeter-wave integrated-circuit (MMIC) amplifiers capable of providing useful amounts of gain over the frequency range from 120 to 180 GHz have been developed as prototype low-noise amplifiers (LNAs) to be incorporated into instruments for sensing cosmic microwave background radiation. There are also potential uses for such LNAs in electronic test equipment, passive millimeter- wave imaging systems, radar receivers, communication receivers, and systems for detecting hidden weapons. The main advantage afforded by these MMIC LNAs, relative to prior MMIC LNAs, is that their coverage of the 120-to-180-GHz frequency band makes them suitable for reuse in a wider variety of applications without need to redesign them. Each of these MMIC amplifiers includes InP transistors and coplanar waveguide circuitry on a 50- mthick chip (see Figure 1). Coplanar waveguide transmission lines are used for both applying DC bias and matching of input and output impedances of each transistor stage. Via holes are incorporated between top and bottom ground planes to suppress propagation of electromagnetic modes in the substrate. On the basis of computational simulations, each of these amplifiers was expected to operate with a small-signal gain of 14 dB and a noise figure of 4.3 dB. At the time of writing this article, measurements of noise figures had not been reported, but on-chip measurements had shown gains approaching their simulated values (see Figure 2).

Pukala, David↗