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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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NASA NTRS1995

A Probabilistic Design Method Applied to Smart Composite Structures

A probabilistic design method is described and demonstrated using a smart composite wing. Probabilistic structural design incorporates naturally occurring uncertainties including those in constituent (fiber/matrix) material properties, fabrication variables, structure geometry and control-related parameters. Probabilistic sensitivity factors are computed to identify those parameters that have a great influence on a specific structural reliability. Two performance criteria are used to demonstrate this design methodology. The first criterion requires that the actuated angle at the wing tip be bounded by upper and lower limits at a specified reliability. The second criterion requires that the probability of ply damage due to random impact load be smaller than an assigned value. When the relationship between reliability improvement and the sensitivity factors is assessed, the results show that a reduction in the scatter of the random variable with the largest sensitivity factor (absolute value) provides the lowest failure probability. An increase in the mean of the random variable with a negative sensitivity factor will reduce the failure probability. Therefore, the design can be improved by controlling or selecting distribution parameters associated with random variables. This can be implemented during the manufacturing process to obtain maximum benefit with minimum alterations.

Shiao, Michael C.↗

DOE OSTI2022

Internal Model-Based Active Damping Strategy for a Back-to-Back Modular Multilevel Converter System for Advanced Grid Support: Preprint

The proposed work focuses on the possibility of achieving the reduction in the size of the interfacing filter for the grid connection for a back-to-back modular multilevel converter system with the use of third order LCL filters. To achieve the same attenuation, it is possible to reduce the size of the interfacing filters by the usage of third order filters. However, this kind of filtering comes with the limitation of having sustained oscillations due to lack of damping especially during transient changes. Therefore, in this work, a methodology has been proposed based on the principle of internal model to cater for this unwanted oscillations. The third order system is modeled inside the microcontroller with the damping enabled. The error between the output from the model and the actual are compared and the error is utilized to accomplish the active damping strategy. The proposed architecture is verified via computer simulations based on MATLAB/Simulink domain and various case study results along with their discussion is presented in this paper.

dq control↗

DOE OSTI2020

¬¬Integration of Quantification of Margins and Uncertainties Methodology into Parallel Discrete Event Simulator Framework

Parallel Discrete Events Simulation (PDES) is becoming increasingly important to lab efforts in security and intelligence. It is used to model complex asynchronous systems such as computer networks, satellite systems, vehicular traffic, and human performance. Uncertainty Quantification (UQ) techniques have become a mainstay of Verification and Validation (V&V) efforts on physics simulations. However, PDES models are very different from traditional physics simulations, and research into UQ techniques for PDES is in its infancy. It is not clear which traditional UQ techniques can be applied to PDES, or what new techniques will need to be developed. The goal of this project was to identify existing UQ techniques that can be applied to PDES, and to develop new techniques as necessary. The project implemented or developed techniques to handle issues that do not appear in traditional physics simulations, but are common among PDES, including sampling techniques for high-dimensional homogenous inputs, response surfaces for high-variance heteroskedastic output, and characterization of skewed output distributions. These are foundational UQ techniques that must be used in any complete UQ analysis (Tong 2018).

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2013

The Effect of Scale Dependent Discretization on the Progressive Failure of Composite Materials Using Multiscale Analyses

A multiscale modeling methodology, which incorporates a statistical distribution of fiber strengths into coupled micromechanics/ finite element analyses, is applied to unidirectional polymer matrix composites (PMCs) to analyze the effect of mesh discretization both at the micro- and macroscales on the predicted ultimate tensile (UTS) strength and failure behavior. The NASA code FEAMAC and the ABAQUS finite element solver were used to analyze the progressive failure of a PMC tensile specimen that initiates at the repeating unit cell (RUC) level. Three different finite element mesh densities were employed and each coupled with an appropriate RUC. Multiple simulations were performed in order to assess the effect of a statistical distribution of fiber strengths on the bulk composite failure and predicted strength. The coupled effects of both the micro- and macroscale discretizations were found to have a noticeable effect on the predicted UTS and computational efficiency of the simulations.

Ricks, Trenton M.↗

DOE OSTI2024

Consistent and reproducible computation of the glass transition temperature from molecular dynamics simulations

In many fields, from semiconductors for opto-electronic applications to ionic liquids (ILs) for separations, the glass transition temperature (Tg) of a material is a useful gauge for its potential use in practical settings. As a result, there is a great deal of interest in predicting Tg using molecular simulations. However, the uncertainty and variation in the trend shift method, a common approach in simulations to predict Tg, can be high. This is due to the need for human intervention in defining a fitting range for linear fits of density with temperature assumed for the liquid and glass phases across the simulated cooling. The definition of such fitting ranges then defines the estimate for the Tg as the intersection of linear fits. We eliminate this need for human intervention by leveraging the Shapiro–Wilk normality test and proposing an algorithm to define the fitting ranges and, consequently, Tg. Through this integration, we incorporate into our automated methodology that residuals must be normally distributed around zero for any fit, a requirement that must be met for any regression problem. Consequently, fitting ranges for realizing linear fits for each phase are statistically defined rather than visually inferred, obtaining an estimate for Tg without any human intervention. The method is also capable of finding multiple linear regimes across density vs temperature curves. We compare the predictions of our proposed method across multiple IL and semiconductor molecular dynamics simulation results from the literature and compare other proposed methods for automatically detecting Tg from density–temperature data. We believe that our proposed method would allow for more consistent predictions of Tg. We make this methodology available and open source through GitHub.

Chemistry↗

DOE OSTI2022

A Subspace-Inclusive Sampling Method for the Computational Design of Compositionally Graded Alloys

Abstract Compositionally graded alloys, a subclass of functionally graded materials (FGMs), utilize localized variations in composition with a single metal part to achieve higher performance than traditional single material parts. In previous work [Kirk, T., Galvan, E., Malak, R., and Arroyave, R., 2018, “Computational Design of Gradient Paths in Additively Manufactured Functionally Graded Materials,” J. Mech. Des., 140, p. 111410. 10.1115/1.4040816], the authors presented a computational design methodology that avoids common issues which limit a gradient alloy’s feasibility, such as deleterious phases, and optimizes for performance objectives. However, the previous methodology only samples the interior of a composition space, meaning designed gradients must include all elements in the space throughout the gradient. Because even small amounts of additional alloying elements can introduce new deleterious phases, this characteristic often neglects potentially simpler solutions to otherwise unsolvable problems and, consequently, discourages the addition of new elements to the state space. The present work improves upon the previous methodology by introducing a sampling method that includes subspaces with fewer elements in the design search. The new method samples within an artificially expanded form of the state space and projects samples outside the true region to the nearest true subspace. This method is evaluated first by observing the sample distribution in each subspace of a 3D, 4D, and 5D state space. Next, a parametric study in a synthetic 3D problem compares the performance of the new sampling scheme to the previous methodology. Lastly, the updated methodology is applied to design a gradient from stainless steel to equiatomic NiTi that has practical uses such as embedded shape memory actuation and for which the previous methodology fails to find a feasible path.

Engineering↗

DOE OSTI2022

Preliminary Thermal-hydraulics and Selected Safety Analysis for Holos-Quad Reactor Design

This report presents thermal hydraulics and safety analyses of the 10 MWe Holos-Quad micro-reactor design developed by HolosGen LLC. These analyses were executed under steady-state and station black-out (SBO) transient conditions using the System Analysis Module (SAM), with a focus on ensuring peak fuel temperatures do not exceed the safety thresholds of 1,250°C for steady state normal operating conditions and 1,600°C for transient conditions. For steady-state temperature predictions, a coupled 1D fluid-to-3D solid heat conduction methodology was used. The steady-state simulations included a model of the central core region where power peaking occurs to predict the maximum steady-state fuel temperature, and a coarse-mesh full-core model to provide the initial condition for the consequent transient SBO decay heat removal simulation. The maximum fuel temperature predicted from the central core region simulations under the normal operating condition was 1,222°C, which is below the design limit 1,250°C. To simulate a SBO transient, a full core model was necessary because of the non-axial symmetric core design and heat loss from the outer core structures to the environment. To reduce the required computational costs, a novel scaled subassembly approach was used to model the full core domain. With the full core steady-state temperature distribution provided as the initial condition, the passive decay heat removal of the core was simulated with the scaled subassembly full core model under SBO transient conditions. This analysis showed that the peak fuel temperatures of the Holos-Quad core remained below their steady-state values during the transient, therefore much lower than the safety limit 1,600°C. This is mainly due to the large thermal inertia of the graphite matrix, relatively low power density, and the large surface-to-volume-ratio of the core. The preliminary analyses presented in this report confirm the inherent safety of the Holos-Quad micro-reactor thermal-hydraulics design, as the peak fuel temperatures under both normal and off-normal operational conditions remain within design and safety limits.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS1994

The software analysis project for the Office of Human Resources

There were two major sections of the project for the Office of Human Resources (OHR). The first section was to conduct a planning study to analyze software use with the goal of recommending software purchases and determining whether the need exists for a file server. The second section was analysis and distribution planning for retirement planning computer program entitled VISION provided by NASA Headquarters. The software planning study was developed to help OHR analyze the current administrative desktop computing environment and make decisions regarding software acquisition and implementation. There were three major areas addressed by the study: current environment new software requirements, and strategies regarding the implementation of a server in the Office. To gather data on current environment, employees were surveyed and an inventory of computers were produced. The surveys were compiled and analyzed by the ASEE fellow with interpretation help by OHR staff. New software requirements represented a compilation and analysis of the surveyed requests of OHR personnel. Finally, the information on the use of a server represents research done by the ASEE fellow and analysis of survey data to determine software requirements for a server. This included selection of a methodology to estimate the number of copies of each software program required given current use and estimated growth. The report presents the results of the computing survey, a description of the current computing environment, recommenations for changes in the computing environment, current software needs, management advantages of using a server, and management considerations in the implementation of a server. In addition, detailed specifications were presented for the hardware and software recommendations to offer a complete picture to OHR management. The retirement planning computer program available to NASA employees will aid in long-range retirement planning. The intended audience is the NASA civil service employee with several years until retirement. The employee enters current salary and savings information as well as goals concerning salary at retirement, assumptions on inflation, and the return on investments. The program produces a picture of the employee's retirement income from all sources based on the assumptions entered. A session showing features of the program was conducted for key personnel at the Center. After analysis, it was decided to offer the program through the Learning Center starting in August 1994.

Tureman, Robert L., Jr.↗

NASA NTRS2003

Enhancement and Extension of Porosity Model in the FDNS-500 Code to Provide Enhanced Simulations of Rocket Engine Components

In the past, the design of rocket engines has primarily relied on the cold flow/hot fire test, and the empirical correlations developed based on the database from previous designs. However, it is very costly to fabricate and test various hardware designs during the design cycle, whereas the empirical model becomes unreliable in designing the advanced rocket engine where its operating conditions exceed the range of the database. The main goal of the 2nd Generation Reusable Launching Vehicle (GEN-II RLV) is to reduce the cost per payload and to extend the life of the hardware, which poses a great challenge to the rocket engine design. Hence, understanding the flow characteristics in each engine components is thus critical to the engine design. In the last few decades, the methodology of computational fluid dynamics (CFD) has been advanced to be a mature tool of analyzing various engine components. Therefore, it is important for the CFD design tool to be able to properly simulate the hot flow environment near the liquid injector, and thus to accurately predict the heat load to the injector faceplate. However, to date it is still not feasible to conduct CFD simulations of the detailed flowfield with very complicated geometries such as fluid flow and heat transfer in an injector assembly and through a porous plate, which requires gigantic computer memories and power to resolve the detailed geometry. The rigimesh (a sintered metal material), utilized to reduce the heat load to the faceplate, is one of the design concepts for the injector faceplate of the GEN-II RLV. In addition, the injector assembly is designed to distribute propellants into the combustion chamber of the liquid rocket engine. A porosity mode thus becomes a necessity for the CFD code in order to efficiently simulate the flow and heat transfer in these porous media, and maintain good accuracy in describing the flow fields. Currently, the FDNS (Finite Difference Navier-Stakes) code is one of the CFD codes which are most widely used by research engineers at NASA Marshall Space Flight Center (MSFC) to simulate various flow problems related to rocket engines. The objective of this research work during the 10-week summer faculty fellowship program was to 1) debug the framework of the porosity model in the current FDNS code, and 2) validate the porosity model by simulating flows through various porous media such as tube banks and porous plate.

Cheng, Gary↗

DOE OSTI2026

Comparative Analysis of Model Predictive Control and MPC-Informed Rule-Based Control for Thermal Storage Operation in Ultra-Low Temperature 4th Generation District Heating Networks

The integration of thermal storage and heat pumps in district heating networks (DHNs) can significantly enhance operational flexibility and energy efficiency; however, the practical deployment of advanced control strategies is often hindered by forecasting requirements and computational complexity. This study presents a comparative analysis of thermal storage control strategies in an ultra-low-temperature fourth-generation DHN, focusing on the development of a simplified rule-based control (RBC) explicitly informed by Model Predictive Control (MPC) behavior. The proposed methodology systematically analyzes the charging and discharging decisions of an MPC-controlled system under ideal forecasting conditions and extracts recurrent control patterns as a function of key system variables, including outdoor temperature, thermal demand, and electricity price. These patterns are translated into a set of structured time- and condition-based rules, resulting in an MPC-informed RBC that embeds predictive insights while preserving implementation simplicity and operational transparency. The approach is validated on a realistic mixed-use urban district in Denver, Colorado, USA, equipped with a centralized air-source heat pump, distributed water-to-water heat pumps, and a central thermal storage unit. Results show that the tuned RBC attains approximately 96% of ideal MPC economic performance (-27% of costs), preserves values of technical and environmental indicators (reduction only of 2-3%), and substantially reduces complexity. Sensitivity analyses further demonstrate the robustness of the RBC under varying operational conditions (i.e., ambient temperature, electricity price). Overall, the study demonstrates that MPC-informed rule-based control represents an effective trade-off between control performance and real-world applicability, enabling the integration of additional system components while maintaining simplicity, robustness, and ease of implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2013

"Green Planet Architecture"-A Methodology for Self-Sustainable Distributed Renewable Energy Ecosystems

This planet has been endowed with a host of natural mechanisms to keep the environment and climate in balance. Humans are now facing the need to restore this balance that has been upset in the past years because of a growing population and resource demands. To steer dependency away from freshwater crops and decrease environmental damage from humanity s fuel and energy demands, it is necessary to take advantage of the natural adaptive biomass resources that are already in place. Using methods of "Green Planet Architecture," based on compilations of current research and procedures, could lead to new forms of energy and fueling as well as new sources for food and feed. Green Planet Architecture involves climatic adaptive biomass; geospatial intelligence; agri- and aqua-culture life cycles; and soil, wetland, and shoreline restoration. Plants such as Salicornia, seashore mallow, castor, mangroves, and perhaps Moringa can be modified (naturally, model-assisted, or genetically) to thrive in salt water and brackish water or otherwise not arable conditions, making them potentially new crops that will not displace traditional farming. These fueling sources also have potential to be used in other rapid-growth industries, such as the aviation industry, that have incentive to move towards more sustainable fuel supplies. This report highlights an example of how synergistic development of biomass resources and geospatial intelligence high-performance computing capabilities can be focused to resolve potential drought-famine problems. These techniques provide a basis for future e-science-based discovery (and access) through technology that can be expanded to support global societal applications.

Saxena, Nikita T.↗

DOE OSTI2024

3D Multiresolution Velocity Model Fusion with Probability Graphical Models

ABSTRACT The variability in spatial resolution of seismic velocity models obtained via tomographic methodologies is attributed to many factors, including inversion strategies, ray-path coverage, and data integrity. Integration of such models, with distinct resolutions, is crucial during the refinement of community models, thereby enhancing the precision of ground-motion simulations. Toward this goal, we introduce the probability graphical model (PGM), combining velocity models with heterogeneous resolutions and nonuniform data point distributions. The PGM integrates data relations across varying resolution subdomains, enhancing detail within low-resolution (LR) domains by utilizing information and prior knowledge from high-resolution (HR) subdomains through a maximum posterior problem. Assessment of efficacy, utilizing both 2D and 3D velocity models—consisting of synthetic checkerboard models and a fault-zone model from Ridgecrest, California—demonstrates noteworthy improvements in accuracy, compared to state-of-the-art fusion techniques. Specifically, we find reductions of 30% and 44% in computed travel-time residuals for 2D and 3D models, respectively, as compared to conventional smoothing techniques. Unlike conventional methods, the PGM’s adaptive weight selection facilitates preserving and learning details from complex, nonuniform HR models and applies the enhancements to the LR background domain.

Geochemistry & Geophysics↗

NASA NTRS2015

Determination of the SNPP VIIRS SDSM Screen Relative Transmittance From Both Yaw Maneuver and Regular On-Orbit Data

The Visible Infrared Imaging Radiometer Suiteaboard the Suomi National Polar-orbiting Partnership (SNPP) satellite performs radiometric calibration of its reflective solar bands primarily through observing a sunlit onboard solar diffuser (SD). The SD bidirectional reflectance distribution function(BRDF) degradation factor is determined by an onboard SD stability monitor (SDSM), which observes the Sun through a pinhole screen and the sunlit SD. The transmittance of the SDSM pinhole screen over a range of solar angles was determined prelaunch and used initially to determine the BRDF degradation factor.The degradation-factor-versus-time curves were found to have a number of very large unphysical undulations likely due to the inaccuracy in the prelaunch determined SDSM screen transmittance.To refine the SDSM screen transmittance, satellite yaw maneuvers were carried out. With the SDSM screen relative transmittance determined from the yaw maneuver data, the computed BRDFdegradation factor curves still have large unphysical ripples, indicating that the projected solar horizontal angular step size in the yaw maneuver data is too large to resolve the transmittance at a fine angular scale. We develop a methodology to use both the yaw maneuver and a small portion of regular on-orbit data to determine the SDSM screen relative transmittance at a fine angular scale. We determine that the error standard deviation of the calculated relative transmittance ranges from 0.00030 (672 nm) to 0.00092 (926 nm). With the newly determined SDSM screen relative transmittance, the computed BRDF degradation factor behaves much more smoothly over time.

Lei, Ning↗

DOE OSTI2026

Benchtop Autonomous Electrochemical Characterization System for Combinatorial Thin-Film Solid Oxide Electrodes

The design of materials for electrochemical energy conversion is complicated by a vast search space of candidate materials and multifaceted property requirements: multicarrier conductivity, stability, and catalytic activity are all necessary but rarely intersect. Although self-driving laboratories are rapidly rising to address such material optimization problems, the required infrastructure for integrated, large-scale robotic facilities can be cost-prohibitive. Here we develop and evaluate a closed-loop measurement system for efficient screening of proton-conducting oxide electrodes for ceramic fuel cells and electrolyzers, building on top of an existing benchtop instrument and integrating techniques for rapid impedance measurement and automated analysis. This system exemplifies a “minimum viable” self-driving implementation that can deliver substantial benefits with relatively simple infrastructure. Combinatorial thin-film microelectrode libraries are characterized with a recently developed joint time-domain and frequency-domain impedance measurement technique, which provides an order-of-magnitude acceleration relative to conventional impedance spectroscopy. The distribution of relaxation times is extracted from impedance data and analyzed without human intervention. These results feed an active learning and Bayesian optimization process that learns to predict electrochemical impedance as a function of material composition, measurement temperature, oxygen partial pressure, and electrical bias, which further reduces the screening time by tenfold with optimized experimental sequences. We apply this system to Ba⁡(Co,Fe,Zr,Y)⁢O 3−𝛿 combinatorial libraries and evaluate its effectiveness for learning material property trends and optimizing expensive-to-evaluate properties such as activation energy. This offers insights into key methodological aspects of practical autonomous experimentation, including surrogate model validation, cost-aware acquisition functions, and high-throughput data interpretation. Our results demonstrate the efficacy of the system for rapidly gathering information, but also highlight real-world experimental challenges of thin-film degradation and numerical instability in surrogate models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Subnanometer Scale Mapping of Hydrogen Doping in Vanadium Dioxide

Hydrogen donor doping of correlated electron systems such as vanadium dioxide (VO 2 ) profoundly modifies the ground state properties. The electrical behavior of H x VO 2 is strongly dependent on the hydrogen concentration; hence, atomic scale control of the doping process is necessary. It is however a nontrivial problem to quantitatively probe the hydrogen distribution in a solid matrix. As hydrogen transfers its sole electron to the material, the ionization mechanism is suppressed. Here in this study, a methodology mapping the doping distribution at subnanometer length scale is demonstrated across a H x VO 2 thin film focusing on the oxygen-hydrogen bonds using electron energy loss spectroscopy (EELS) coupled with first-principles EELS calculations. The hydrogen distribution was revealed to be nonuniform along the growth direction and between different VO 2 grains, calling for intricate hydrogenation mechanisms. Our study points to a powerful approach to quantitatively map dopant distribution in quantum materials relevant to energy and information science

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS2012

Green Planet Architecture - A Methodology for Self-Sustainable Distributed Renewable Energy Ecosystems

Our planet has been endowed with a host of natural mechanisms to keep the environment and climate in balance. Humans are now facing the need to restore this balance that has been upset in the past years because of a growing population and resource demands. To steer dependency away from freshwater crops and decrease environmental damage from humanity s fuel and energy demands, it is necessary to take advantage of the natural adaptive biomass resources that are already in place. Using methods of Green Planet Architecture, based on compilations of current research and procedures, could lead to new forms of energy and fueling as well as new sources for food and feed. Green Planet Architecture involves climatic adaptive biomass; geospatial intelligence; agri- and aqua-culture life cycles; and soil, wetland, and shoreline restoration. Plants such as Salicornia, seashore mallow, castor, mangroves, and perhaps Moringa can be modified (natural, model-assisted, or genetically modified) to thrive in salt-water and brackish water or otherwise not arable conditions, making them potentially new crops that will not displace traditional farming. These fueling sources also have potential to be used in other rapid-growth industries, such as the aviation industry, that have incentive to move towards more sustainable fuel supplies. This paper highlights an example of how synergistic development of biomass resources and geospatial intelligence high-performance computing capabilities can be focused to resolve potential drought-famine problems. These techniques, provide a basis for future e-science-based discovery (and access) through technology that can be expanded to support global societal applications.

Saxena, Nikita T.↗

DOE OSTI2024

Adaptive Online Model Update Algorithm for Predictive Control in Networked Systems

In this article, we introduce an adaptive on-line model update algorithm designed for predictive control applications in networked systems, particularly focusing on power distribution systems. Unlike traditional methods that depend on historical data for offline model identification, our approach utilizes real-time data for continuous model updates. This method integrates seamlessly with existing online control and optimization algorithms and provides timely updates in response to real-time changes. This methodology offers significant advantages, including a reduction in the communication network bandwidth requirements by minimizing the data exchanged at each iteration and enabling the model to adapt after disturbances. Furthermore, our algorithm is tailored for non-linear convex models, enhancing its applicability to practical scenarios. The efficacy of the proposed method is validated through a numerical study, demonstrating improved control performance using a synthetic IEEE test case.

data-driven model predictive control↗

DOE OSTI2022

Deep compressed seismic learning for fast location and moment tensor inferences with natural and induced seismicity

Fast detection and characterization of seismic sources is crucial for decision-making and warning systems that monitor natural and induced seismicity. However, besides the laying out of ever denser monitoring networks of seismic instruments, the incorporation of new sensor technologies such as Distributed Acoustic Sensing (DAS) further challenges our processing capabilities to deliver short turnaround answers from seismic monitoring. In response, this work describes a methodology for the learning of the seismological parameters: location and moment tensor from compressed seismic records. In this method, data dimensionality is reduced by applying a general encoding protocol derived from the principles of compressive sensing. The data in compressed form is then fed directly to a convolutional neural network that outputs fast predictions of the seismic source parameters. Thus, the proposed methodology can not only expedite data transmission from the field to the processing center, but also remove the decompression overhead that would be required for the application of traditional processing methods. An autoencoder is also explored as an equivalent alternative to perform the same job. We observe that the CS-based compression requires only a fraction of the computing power, time, data and expertise required to design and train an autoencoder to perform the same task. Implementation of the CS-method with a continuous flow of data together with generalization of the principles to other applications such as classification are also discussed.

54 ENVIRONMENTAL SCIENCES↗