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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Structural Analysis of Lightning Protection System for New Launch Vehicle

This project includes the design and specification of a lightning protection system for Launch Complex 39 B (LC39B) at Kennedy Space Center, FL in support of the Constellation Program. The purpose of the lightning protection system is to protect the Crew Launch Vehicle (CLV) or Cargo Launch Vehicle (CaLV) and associated launch equipment from direct lightning strikes during launch processing and other activities prior to flight. The design includes a three-tower, overhead catenary wire system to protect the vehicle and equipment on LC39B as described in the study that preceded this design effort: KSC-DX-8234 "Study: Construct Lightning Protection System LC3 9B". The study was a collaborative effort between Reynolds, Smith, and Hills (RS&H) and ASRC Aerospace (ASRC), where ASRC was responsible for the theoretical design and risk analysis of the lightning protection system and RS&H was responsible for the development of the civil and structural components; the mechanical systems; the electrical and grounding systems; and the siting of the lightning protection system. The study determined that a triangular network of overhead catenary cables and down conductors supported by three triangular free-standing towers approximately 594 ft tall (each equipped with a man lift, ladder, electrical systems, and communications systems) would provide a level of lightning protection for the Constellation Program CLV and CaLV on Launch Pad 39B that exceeds the design requirements.

Cope, Anne↗
DOE OSTI2026

Thiol post-translational modifications modulate allosteric regulation of the OpcA–G6PDH complex through conformational gate control

In cyanobacteria, the redox-sensitive protein OpcA acts as a metabolic switch for G6PDH, enabling rapid adjustment of reducing power generation from glycogen catabolism and thereby precisely regulating carbon flux between anabolic and catabolic pathways. Although redox-sensitive cysteines in OpcA are known to regulate G6PDH, the mechanisms by which redox post-translational modifications (PTMs) on OpcA control G6PDH structure and activity remain unclear. Here, we combine computational modeling with experimental redox proteomics in Synechococcus elongatus PCC 7942 to dissect this mechanism. Experimentally, redox proteome analysis revealed differential redox PTM patterns, particularly on cysteines within the G6PDH-binding site of OpcA. These environmentally sensitive PTM changes at the interface suggest that thiol modifications in this region form a key regulatory node. More broadly, redox proteomics identified site-specific cysteine modifications under light/dark transitions and circadian cycling, linking distinct redox regimes to discrete PTM states. We employed PTM-Psi simulations to show that thiol PTMs near the OpcA–G6PDH interface are critical for allosteric regulation of G6PDH. The thiol PTMs on OpcA affect a putative gate region in G6PDH for substrate ingress and product egress as well as key hydrogen-bond networks within the active site. We infer that PTMs on OpcA tune the conformational landscapes of individual G6PDH subunits toward functionally relevant configurations according to environmental gradients, biasing the enzyme toward catalytically favorable states. Together, our results reveal a molecular mechanism in which thiol PTMs on OpcA modulate G6PDH structure and function through PTM-induced reorganization of conformational dynamics and allosteric communication. These findings demonstrate that PTM-level regulation provides a critical control layer from genotypes to phenotypes that enables cyanobacteria to rapidly adapt to environmental fluctuations through precise metabolic fine-tuning.

Allosteric regulation↗
DOE OSTI2021

Collective Nanoparticle Dynamics Associated with Bridging Network Formation in Model Polymer Nanocomposites

The addition of nanoparticles (NPs) to polymers is a powerful method to improve the mechanical and other properties of macromolecular materials. Such hybrid polymer–particle systems are also rich in fundamental soft matter physics. Among several factors contributing to mechanical reinforcement, a polymer-mediated NP network is considered to be the most important in polymer nanocomposites (PNCs). Here, we present an integrated experimental–theoretical study of the collective NP dynamics in model PNCs using X-ray photon correlation spectroscopy and microscopic statistical mechanics theory. Silica NPs dispersed in unentangled or entangled poly(2-vinylpyridine) matrices over a range of NP loadings are used. Static collective structure factors of the NP subsystems at temperatures above the bulk glass transition temperature reveal the formation of a network-like microstructure via polymer-mediated bridges at high NP loadings above the percolation threshold. The NP collective relaxation times are up to 3 orders of magnitude longer than the self-diffusion limit of isolated NPs and display a rich dependence with observation wavevector and NP loading. A mode-coupling theory dynamical analysis that incorporates the static polymer-mediated bridging structure and collective motions of NPs is performed. It captures well both the observed scattering wavevector and NP loading dependences of the collective NP dynamics in the unentangled polymer matrix, with modest quantitative deviations emerging for the entangled PNC samples. Here, we identify an unusual and weak temperature dependence of collective NP dynamics, in qualitative contrast with the mechanical response. Hence, the present study has revealed key aspects of the collective motions of NPs connected by polymer bridges in contact with a viscous adsorbing polymer medium and identifies some outstanding remaining challenges for the theoretical understanding of these complex soft materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Knowledge-matching based computational framework for genome-scale metabolic model refinement

Genome-scale metabolic models (GEMs) are mathematically structured knowledge base reconstructed from annotated genome of different organisms. With the advancement of next-generation sequencing technology, many organisms have had their genomes sequenced. However, obtaining a high-quality GEM is highly time-consuming, even with the introduction of several genome-scale reconstruction tools that offer automated draft network generation and gap filling. It has been recognized that the iterative process of manual curation and refinement is the limiting step of GEM development, and how to expedite the GEM refinement is still an open question. As cellular metabolism is a complex system with very high degree of freedom and redundancy, the principles and techniques developed in process systems engineering can be adapted to expedite GEM refinement. In this paper we present a knowledge-matching based computation framework for GEM refinement, and demonstrate the effectiveness of the proposed solution using the refinement of a GEM for Clostridium tyrobutyricum.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Model-Free Voltage Control Approach to Mitigate Motor Stalling and FIDVR for Smart Grids

Electric power networks are large and highly nonlinear dynamical systems that present unique challenges to control design. Though there is a large number of dynamic models for power system stability and control, many models are only useful with right assumptions and wrong for other tasks. Moreover, the dynamic behavior of the grid is increasingly complex under the banner of smart grids. These lead to the difficulty of developing appropriate dynamic modeling, and thus an efficient control strategy. To avoid such modeling challenges, here we present a novel dynamic voltage control strategy based on a model-free control (MFC) approach, requiring no modeling procedure. In particular, it focuses on fault-induced delayed voltage recovery (FIDVR) events, which require complex and accurate dynamic load models to replicate such events. This work utilizes MFC as an online controller to achieve the desired voltage stability under the FIDVR event. The proposed MFC strategy allows simple implementation and low computational cost for efficient mitigation of FIDVR. For benchmarking, a reasonably accurate dynamic performance model is explored. Simulation results with the IEEE 57 bus test network demonstrate the enhanced dynamic voltage profile for load buses having induction motors with the support of reactive power resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Surface-Functionalized Cellulose Nanocrystals as Nanofillers for Crosslinking Processes: Implications for Thermosetting Resins

Understanding the response of fillers in the epoxy resin crosslinking process and characterizing polymer–filler dynamics are the key features that guide the engineering of new thermosetting resin composites. Here, in this work, X-ray photon correlation spectroscopy (XPCS) is used as a thermal analysis tool to track the microscopic changes occurring during the cure of a functionalized cellulose nanocrystal (mCNC)–epoxy composite. In contrast, traditional methods such as differential scanning calorimetry (DSC) and curing rheology are used to understand the kinetics and properties on macroscopic length scales. Of interest is the influence of the mCNC on the curing kinetics and properties of the thermosetting resin. Two levels of modification (increasing hydrophobicity) were chosen to observe the effect of functionalization. Before cure, the highly functionalized CNC (mCNC3) shows a 44% increase in complex viscosity (η*), while the less functionalized CNC (mCNC2) shows a η* value similar to that of the neat resin. As the cure cycle progresses, results from DSC, rheology, and XPCS further show the enhancement in dispersion for mCNC3. The results show a clear difference in the maximum drift velocity, maximum heat flow, and complex viscosity during the ramp to the isothermal cure temperature (T cure ). Such results suggest that mCNC3 contains a well-dispersed network of particles due to the higher level of functionalization. During T cure , a transition in elastic modulus (G') occurs only for the highly functionalized CNC particle system. We believe that heat-induced aggregation occurs, and the crosslinked resin ultimately dominates the macroscopic properties of the final cured system for all samples. The results from the three techniques are in good agreement and showcase XPCS as a beneficial experimental tool for characterizing the microscopic dynamics of particulate-filled thermosetting resins. Hence, we envision this to be a fundamental curing study for the design of thermosetting resin composites.

36 MATERIALS SCIENCE↗
NASA NTRS2013

Practical Application of PRA as an Integrated Design Tool for Space Systems

This paper presents the application of the first comprehensive Probabilistic Risk Assessment (PRA) during the design phase of a joint NASA/NOAA weather satellite program, Geostationary Operational Environmental Satellite Series R (GOES-R). GOES-R is the next generation weather satellite primarily to help understand the weather and help save human lives. PRA has been used at NASA for Human Space Flight for many years. PRA was initially adopted and implemented in the operational phase of manned space flight programs and more recently for the next generation human space systems. Since its first use at NASA, PRA has become recognized throughout the Agency as a method of assessing complex mission risks as part of an overall approach to assuring safety and mission success throughout project lifecycles. PRA is now included as a requirement during the design phase of both NASA next generation manned space vehicles as well as for high priority robotic missions. The influence of PRA on GOES-R design and operation concepts are discussed in detail. The GOES-R PRA is unique at NASA for its early implementation. It also represents a pioneering effort to integrate risks from both Spacecraft (SC) and Ground Segment (GS) to fully assess the probability of achieving mission objectives. PRA analysts were actively involved in system engineering and design engineering to ensure that a comprehensive set of technical risks were correctly identified and properly understood from a design and operations perspective. The analysis included an assessment of SC hardware and software, SC fault management system, GS hardware and software, common cause failures, human error, natural hazards, solar weather and infrastructure (such as network and telecommunications failures, fire). PRA findings directly resulted in design changes to reduce SC risk from micro-meteoroids. PRA results also led to design changes in several SC subsystems, e.g. propulsion, guidance, navigation and control (GNC), communications, mechanisms, and command and data handling (C&DH). The fault tree approach assisted in the development of the fault management system design. Human error analysis, which examined human response to failure, indicated areas where automation could reduce the overall probability of gaps in operation by half. In addition, the PRA brought to light many potential root causes of system disruptions, including earthquakes, inclement weather, solar storms, blackouts and other extreme conditions not considered in the typical reliability and availability analyses. Ultimately the PRA served to identify potential failures that, when mitigated, resulted in a more robust design, as well as to influence the program's concept of operations. The early and active integration of PRA with system and design engineering provided a well-managed approach for risk assessment that increased reliability and availability, optimized lifecyc1e costs, and unified the SC and GS developments.

Kalia, Prince↗
DOE OSTI2018

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

Deep learning (DL), a new generation of artificial neural network research, has transformed industries, daily lives, and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem-relevant features and capture highly complex data distributions. I argue that DL can help address several major new and old challenges facing research in water sciences such as interdisciplinarity, data discoverability, hydrologic scaling, equifinality, and needs for parameter regionalization. This review paper is intended to provide water resources scientists and hydrologists in particular with a simple technical overview, transdisciplinary progress update, and a source of inspiration about the relevance of DL to water. The review reveals that various physical and geoscientific disciplines have utilized DL to address data challenges, improve efficiency, and gain scientific insights. DL is especially suited for information extraction from image-like data and sequential data. Techniques and experiences presented in other disciplines are of high relevance to water research. Meanwhile, less noticed is that DL may also serve as a scientific exploratory tool. A new area termed AI neuroscience , where scientists interpret the decision process of deep networks and derive insights, has been born. This budding subdiscipline has demonstrated methods including correlation-based analysis, inversion of network-extracted features, reduced-order approximations by interpretable models, and attribution of network decisions to inputs. Moreover, DL can also use data to condition neurons that mimic problem-specific fundamental organizing units, thus revealing emergent behaviors of these units. Vast opportunities exist for DL to propel advances in water sciences.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

A Backscatter Moessbauer Spectrometer (BaMS) for use on Mars

The use of Moessbauer spectroscopy for in situ analysis on the surface of Mars was proposed and the design and implementation of a backscatter Moessbauer spectrometer (BaMS) instrument suitable for planetary missions to the surfaces of Mars (MESUR), the Moon (Artemis and lunar outpost), asteroids, or other solid solar system objects is discussed. The BaMS instrument is designed to be capable of analysis of a sample for the mineralogy of its iron-bearing phases without any sample preparation. A requirement of lander missions to Mars is instrumentation for in situ mineralogical analyses. Such analyses provide data needed for primary characterization as to the type of surface materials present and by inference the processes that formed and subsequently modified them. For purposes of providing diagnostic information about naturally occurring materials, the element iron is particularly important because it is abundant and multivalent (primarily 0, +2, and +3 oxidation states). Knowledge of the oxidation state of iron and its distribution among iron-bearing mineralogies tightly constrains the types of materials present. The pivotal role of iron was already recognized in 1978 by COMPLEX, who recommended development of flight instruments that would identify mineralogy and the oxidation state of iron in planetary surface materials. The near-term U.S. strategy for the exploration of Mars is the MESUR (Mars Environmental SURvey) program, which entails emplacement of a network of small, long-lived surface landers. For the Moon, BaMS was recommended as part of a three-instrument landed payload for the Artemis missions, targeted for 1997. BaMS would prospect for ilmenite, an oxygen resource material, and provide data to assess the maturity of lunar soil. Because instrumental characteristics are low mass, low volume, and low power consumption, BaMS is suitable for implementation on even small landers and rovers, as are being envisioned in MESUR and Artemis concepts. In addition to providing highly diagnostic data, a BaMS analyzer is inherently simple and, as is highly desirable for remote operation, no sample preparation is required.

Agresti, D. G.↗
NASA NTRS2002

Mars Radar Observations with the Goldstone Solar System Radar

The Goldstone Solar System Radar (GSSR) has successfully collected radar echo data from Mars over the past 30 years. As such, the GSSR has played a role as a specific mission element within Mars exploration. The older data provided local elevation information for Mars, along with radar scattering information with global resolution. Since the upgrade to the 70-m Deep Space Network (DSN) antenna at Goldstone completed in 1986, Mars data has been collected during all but the 1997 Mars opposition. Radar data, and non-imaging delay-Doppler data in particular, requires significant data processing to extract elevation, reflectivity and roughness of the reflecting surface. The spatial resolution of these experiments is typically some 20 km in longitude by some 150 km in latitude. The interpretation of these parameters while limited by the complexities of electromagnetic scattering, do provide information directly relevant to geophysical and geomorphic analyses of Mars. The usefulness of radar data for Mars exploration has been demonstrated in the past. Radar data were critical in assessing the Viking Lander 1 site as well as, more recently, the Pathfinder landing site. In general, radar data have not been available to the Mars exploration community at large. A project funded initially by the Mars Exploration Directorate Science Office at the Jet Propulsion Laboratory (JPL), and later funded by NASA's Mars Data Analysis Program has reprocessed to a common format a decade's worth of raw GSSR Mars delay-Doppler data in aid of landing site characterization for the Mars Program. These data will soon be submitted to the Planetary Data System (PDS). The radar data used were obtained between 1988 and 1995 by the GSSR, and comprise some 63 delay-Doppler radar tracks. Of these, 15 have yet to be recovered from old 9-track tapes, and some of the data may be permanently lost.

Haldemann, A. F. C.↗
NASA NTRS2003

Aerosol Models for the CALIPSO Lidar Inversion Algorithms

We use measurements and models to develop aerosol models for use in the inversion algorithms for the Cloud Aerosol Lidar and Imager Pathfinder Spaceborne Observations (CALIPSO). Radiance measurements and inversions of the AErosol RObotic NETwork (AERONET1, 2) are used to group global atmospheric aerosols using optical and microphysical parameters. This study uses more than 105 records of radiance measurements, aerosol size distributions, and complex refractive indices to generate the optical properties of the aerosol at more 200 sites worldwide. These properties together with the radiance measurements are then classified using classical clustering methods to group the sites according to the type of aerosol with the greatest frequency of occurrence at each site. Six significant clusters are identified: desert dust, biomass burning, urban industrial pollution, rural background, marine, and dirty pollution. Three of these are used in the CALIPSO aerosol models to characterize desert dust, biomass burning, and polluted continental aerosols. The CALIPSO aerosol model also uses the coarse mode of desert dust and the fine mode of biomass burning to build a polluted dust model. For marine aerosol, the CALIPSO aerosol model uses measurements from the SEAS experiment 3. In addition to categorizing the aerosol types, the cluster analysis provides all the column optical and microphysical properties for each cluster.

Omar, Ali H.↗
NASA NTRS2010

General Pressurization Model in Simscape

System integration is an essential part of the engineering design process. The Ares I Upper Stage (US) is a complex system which is made up of thousands of components assembled into subsystems including a J2-X engine, liquid hydrogen (LH2) and liquid oxygen (LO2) tanks, avionics, thrust vector control, motors, etc. System integration is the task of connecting together all of the subsystems into one large system. To ensure that all the components will "fit together" as well as safety and, quality, integration analysis is required. Integration analysis verifies that, as an integrated system, the system will behave as designed. Models that represent the actual subsystems are built for more comprehensive analysis. Matlab has been an instrument widely use by engineers to construct mathematical models of systems. Simulink, one of the tools offered by Matlab, provides multi-domain graphical environment to simulate and design time-varying systems. Simulink is a powerful tool to analyze the dynamic behavior of systems over time. Furthermore, Simscape, a tool provided by Simulink, allows users to model physical (such as mechanical, thermal and hydraulic) systems using physical networks. Using Simscape, a model representing an inflow of gas to a pressurized tank was created where the temperature and pressure of the tank are measured over time to show the behavior of the gas. By further incorporation of Simscape into model building, the full potential of this software can be discovered and it hopefully can become a more utilized tool.

Servin, Mario↗
NASA NTRS2016

Computational Role of Tunneling in a Programmable Quantum Annealer

Quantum tunneling is a phenomenon in which a quantum state tunnels through energy barriers above the energy of the state itself. Tunneling has been hypothesized as an advantageous physical resource for optimization. Here we present the first experimental evidence of a computational role of multiqubit quantum tunneling in the evolution of a programmable quantum annealer. We developed a theoretical model based on a NIBA Quantum Master Equation to describe the multi-qubit dissipative cotunneling effects under the complex noise characteristics of such quantum devices.We start by considering a computational primitive, the simplest non-convex optimization problem consisting of just one global and one local minimum. The quantum evolutions enable tunneling to the global minimum while the corresponding classical paths are trapped in a false minimum. In our study the non-convex potentials are realized by frustrated networks of qubit clusters with strong intra-cluster coupling. We show that the collective effect of the quantum environment is suppressed in the critical phase during the evolution where quantum tunneling decides the right path to solution. In a later stage dissipation facilitates the multiqubit cotunneling leading to the solution state. The predictions of the model accurately describe the experimental data from the D-WaveII quantum annealer at NASA Ames. In our computational primitive the temperature dependence of the probability of success in the quantum model is opposite to that of the classical paths with thermal hopping. Specially, we provide an analysis of an optimization problem with sixteen qubits,demonstrating eight qubit cotunneling that increases success probabilities. Furthermore, we report results for larger problems with up to 200 qubits that contain the primitive as subproblems.

hard problems↗
DOE OSTI2023

The Convergence of Vehicle Electrification and Automation - Enabling Sustainable Automated Mobility Districts

The transportation industry is entering a new frontier with three major simultaneous transformations: electrification, automation, and on-demand services. All three directly affect the same spaces of vehicle technology, infrastructure, and system operational performance. Further, each area of change adds its own driving forces of complexity to the others' design challenges. This paper explores these transformations, framing the implications for vehicle technology and infrastructure and their impact on operating concepts and strategies, which in turn impact the sustainability of the transportation infrastructure in the built environment. Specifically, this paper examines battery-electric vehicles operating in a fully automated fleet within an "automated mobility district" (AMD) setting with multiple on-demand services. From this examination, the paper assesses the implications for transportation electrification, comparing and contrasting traditional fixed-route services with those of on-demand transit and automated transit networks, including the trade-offs between vehicle range (battery capacity), charging parameters, power availability, and operations predictability with respect to policy and infrastructure configurations. The paper also addresses the configuration and management of boarding and alighting zones (also called pickup and drop-off zones), and the need for complementary infrastructure intelligence at intersections and curbs. Finally, conclusions are drawn with respect to the system integration and analysis needs for AMDs regarding resilience and sustainability of mobility infrastructure.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Disentangling atmospheric, hydrological, and coupling uncertainties in compound flood modeling within a coupled Earth system model

Compound riverine and coastal flooding is usually driven by complex interactions among meteorological, hydrological, and ocean extremes. However, existing efforts to model this phenomenon often do not integrate hydrological processes across atmosphere–land–river–ocean systems, leading to substantial uncertainties that have not been fully examined. To bridge this gap, we leverage the new capabilities of the Energy Exascale Earth System Model (E3SM) that enable a multi-component framework that integrates coastally refined atmospheric, terrestrial, and oceanic components. We evaluate compound uncertainties arising from two-way land–river–ocean coupling in E3SM and track the cascading meteorological and hydrological uncertainties through ensemble simulations over the Delaware River basin and estuary during Hurricane Irene (2011). Our findings highlight the importance of two-way river–ocean coupling to compound flood modeling and demonstrate E3SM's capability in capturing compound flood extent near the coast, with a hit rate over 0.75. Our study shows the growing uncertainties that transition from atmospheric forcings to flood distribution and severity. Furthermore, an analysis based on artificial neural networks is used to assess the roles of hydrological drivers, such as infiltration and soil moisture, in the generation of compound flooding. The response of compound floods to tropical cyclones (TCs) is found to be susceptible to these often overlooked drivers. For instance, the flooded area could increase more than 2-fold (∼2.4) if Hurricane Irene were preceded by an extreme antecedent soil moisture condition (AMC). The results not only support the use of a multi-component framework for interactive flooding processes, but also underscore the necessity of broader definitions of compound flooding that encompass the simultaneous occurrence of intense precipitation, storm surge, and high AMC during TCs.

Feng, Dongyu [Pacific Northwest National Laborator↗
NASA NTRS2004

NASA Tech Briefs, April 2004

Topics covered include: Analysis of SSEM Sensor Data Using BEAM; Hairlike Percutaneous Photochemical Sensors; Video Guidance Sensors Using Remotely Activated Targets; Simulating Remote Sensing Systems; EHW Approach to Temperature Compensation of Electronics; Polymorphic Electronic Circuits; Micro-Tubular Fuel Cells; Whispering-Gallery-Mode Tunable Narrow-Band-Pass Filter; PVM Wrapper; Simulation of Hyperspectral Images; Algorithm for Controlling a Centrifugal Compressor; Hybrid Inflatable Pressure Vessel; Double-Acting, Locking Carabiners; Position Sensor Integral with a Linear Actuator; Improved Electromagnetic Brake; Flow Straightener for a Rotating-Drum Liquid Separator; Sensory-Feedback Exoskeletal Arm Controller; Active Suppression of Instabilities in Engine Combustors; Fabrication of Robust, Flat, Thinned, UV-Imaging CCDs; Chemical Thinning Process for Fabricating UV-Imaging CCDs; Pseudoslit Spectrometer; Waste-Heat-Driven Cooling Using Complex Compound Sorbents; Improved Refractometer for Measuring Temperatures of Drops; Semiconductor Lasers Containing Quantum Wells in Junctions; Phytoplankton-Fluorescence-Lifetime Vertical Profiler; Hexagonal Pixels and Indexing Scheme for Binary Images; Finding Minimum-Power Broadcast Trees for Wireless Networks; and Automation of Design Engineering Processes.

Source record↗
DOE OSTI2018

Two-level Clustering-based Target Detection Through Sensor Deployment and Data Fusion

Target detection is one fundamental problem in many sensor network-based applications, which is typically tackled in two separate stages for sensor deployment and data fusion. We propose an integrated solution, referred to as 2C-SSEM,which combines 2-level clustering-based sensor deployment and source strength estimate map-based data fusion for the detection of a single static or moving target. 2C-SSEM conducts the first level of clustering to determine a sensor deployment scheme and the second level of clustering to divide the deployed sensors into several subsets. For each sensor, the source strength is estimated at each grid point of the entire region based on a signal attenuation model, and for each subset of sensors, the target location is estimated using a distribution map-based statistical analysis method. A final detection decision is made by thresholding the clustering degree of the target location estimates computed by all subsets of sensors. Compared with traditional grid-based target detection methods, 2C-SSEM significantly reduces the computation complexity and improves the detection performance through an integrated optimization strategy. Extensive simulation results show the performance superiority of the proposed solution over several well-known methods for target detection.

Rao, Nageswara↗
DOE OSTI2019

Machine learning framework for assessment of microbial factory performance

Metabolic models can estimate intrinsic product yields for microbial factories, but such frameworks struggle to predict cell performance (including product titer or rate) under suboptimal metabolism and complex bioprocess conditions. On the other hand, machine learning, complementary to metabolic modeling necessitates large amounts of data. Building such a database for metabolic engineering designs requires significant manpower and is prone to human errors and bias. We propose an approach to integrate data-driven methods with genome scale metabolic model for assessment of microbial bio-production (yield, titer and rate). Using engineered E. coli as an example, we manually extracted and curated a data set comprising about 1200 experimentally realized cell factories from ~100 papers. We furthermore augmented the key design features (e.g., genetic modifications and bioprocess variables) extracted from literature with additional features derived from running the genome-scale metabolic model iML1515 simulations with constraints that match the experimental data. Then, data augmentation and ensemble learning (e.g., support vector machines, gradient boosted trees, and neural networks in a stacked regressor model) are employed to alleviate the challenges of sparse, non-standardized, and incomplete data sets, while multiple correspondence analysis/principal component analysis are used to rank influential factors on bio-production. The hybrid framework demonstrates a reasonably high cross-validation accuracy for prediction of E.coli factory performance metrics under presumed bioprocess and pathway conditions (Pearson correlation coefficients between 0.8 and 0.93 on new data not seen by the model).

59 BASIC BIOLOGICAL SCIENCES↗